{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "### Polynomial Regression for Subsurface Data Analytics in Python \n",
    "\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Polynomial Regression for Subsurface Modeling in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of polynomial regression for subsurface modeling workflows. This should help you get started with building subsurface models that data analytics and machine learning. Here's some basic details about linear regression. \n",
    "\n",
    "#### Linear Regression\n",
    "\n",
    "Linear regression for prediction.  Here are some key aspects of linear regression:\n",
    "\n",
    "**Parametric Model**\n",
    "\n",
    "* the fit model is a simple weighted linear additive model based on all the available features, $x_1,\\ldots,x_m$.\n",
    "\n",
    "* the model takes the form of $y = \\sum_{\\alpha = 1}^m b_{\\alpha} x_{\\alpha} + b_0$\n",
    "\n",
    "**Least Squares**\n",
    "\n",
    "* least squares optimization is applied to select the model parameters, $b_1,\\ldots,b_m,b_0$ \n",
    "\n",
    "* we minize the error over the trainind data $\\sum_{i=1}^n (y_i - (\\sum_{\\alpha = 1}^m b_{\\alpha} x_{\\alpha} + b_0))^2$\n",
    "\n",
    "* this could be simplified as the sum of square error over the training data, $\\sum_{i=1}^n (\\Delta y_i)^2$\n",
    "\n",
    "**Assumptions**\n",
    "\n",
    "* **Error-free** - predictor variables are error free, not random variables \n",
    "* **Linearity** - response is linear combination of feature(s)\n",
    "* **Constant Variance** - error in response is constant over predictor(s) value\n",
    "* **Independence of Error** - error in response are uncorrelated with each other\n",
    "* **No multicollinearity** - none of the features are redundant with other features \n",
    "\n",
    "#### Polynomial Regression\n",
    "\n",
    "It can be shown that polynomial regression is just linear regression applied to a polynomial expansion of the predictor features.\n",
    "\n",
    "\\begin{equation}\n",
    "X_{j} \\rightarrow X_{j}, X_{j}^2, X_{j}^3, \\ldots X_{j}^k \n",
    "\\end{equation}\n",
    "\n",
    "where we have $j = 1, \\ldots, m$ original features.\n",
    "\n",
    "We now have a expanded set of predictor features.\n",
    "\n",
    "\\begin{equation}\n",
    "h_{j,k}(X_j) = X_j^k \n",
    "\\end{equation}\n",
    "\n",
    "were we have $j = 1, \\ldots, m$ original features and $k = 1, \\ldots, K$ polynomial orders.  \n",
    "\n",
    "We can now state our model as a linear regression of the transformed features.\n",
    "\n",
    "\\begin{equation}\n",
    "y = f(x) = \\sum_{j=1}^{m} \\sum_{k = 1}^{K} \\beta_{j,k} h_{j,m}(X_j)\n",
    "\\end{equation}\n",
    "\n",
    "So our workflow is:\n",
    "\n",
    "* apply polynomial basis expansion\n",
    "\n",
    "* perform linear regression\n",
    "\n",
    "#### Other Resources\n",
    "\n",
    "This is a tutorial / demonstration of **Linear Regression**.  In $Python$, the $SciPy$ package, specifically the $Stats$ functions (https://docs.scipy.org/doc/scipy/reference/stats.html) provide excellent tools for efficient use of statistics.  \n",
    "I have previously provided this example in R and posted it on GitHub:\n",
    "\n",
    "1. R https://github.com/GeostatsGuy/geostatsr/blob/master/linear_regression_demo_v2.R\n",
    "2. Rmd with docs https://github.com/GeostatsGuy/geostatsr/blob/master/linear_regression_demo_v2.Rmd \n",
    "3. knit as an HTML document(https://github.com/GeostatsGuy/geostatsr/blob/master/linear_regression_demo_v2.html) \n",
    "\n",
    "#### Workflow Goals\n",
    "\n",
    "Learn the basics of time series analysis in Python to for analysis, modeling and prediction with production data. This includes:\n",
    "\n",
    "* Basic Python workflows and data preparation\n",
    "\n",
    "* Training / fitting a linear regression model\n",
    "\n",
    "* Model Checking\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "There are examples below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Import Required Packages\n",
    "\n",
    "Let's import the GeostatsPy package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                   # to set current working directory \n",
    "import numpy as np                                          # arrays and matrix math\n",
    "import scipy                                                # hermite polynomials\n",
    "import scipy.stats as st                                    # statistical methods\n",
    "import pandas as pd                                         # DataFrames\n",
    "import matplotlib.pyplot as plt                             # for plotting\n",
    "from sklearn.linear_model import LinearRegression           # linear regression with scikit learn\n",
    "from sklearn.preprocessing import PolynomialFeatures        # polynomial basis expansion\n",
    "from scipy import stats                                     # statistical summary from a 2D ndarray\n",
    "import seaborn as sns                                       # multivariate statistical displays\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')                           # suppress warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see below) data file in this working directory.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"d:/PGE337\")                                      # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Data\n",
    "\n",
    "Let's load the provided dataset. 'Density_Por_data.csv' is available at https://github.com/GeostatsGuy/GeoDataSets. It is a comma delimited file with 20 density measures ($\\frac{g}{cm^3}$) and porosity measures from 2 rock units from the subsurface, porosity (as a fraction). We load it with the pandas 'read_csv' function into a data frame we called 'df' and then preview it by printing a slice and by utilizing the 'head' DataFrame member function (with a nice and clean format, see below).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Depth</th>\n",
       "      <th>Nporosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.25</td>\n",
       "      <td>-1.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.50</td>\n",
       "      <td>-2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-1.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.25</td>\n",
       "      <td>-0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Depth  Nporosity\n",
       "0   0.25      -1.37\n",
       "1   0.50      -2.08\n",
       "2   0.75      -1.67\n",
       "3   1.00      -1.16\n",
       "4   1.25      -0.24"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"1D_Porosity.csv\")                        # read a .csv file in as a DataFrame  \n",
    "df = pd.read_csv(r\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/1D_Porosity.csv\") # load the data from Dr. Pyrcz's github repository\n",
    "df.head()                                                   # preview the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is useful to review the summary statistics of our loaded DataFrame.  That can be accomplished with the 'describe' DataFrame member function.  We transpose to switch the axes for ease of visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Depth</th>\n",
       "      <td>40.0</td>\n",
       "      <td>5.12500</td>\n",
       "      <td>2.922613</td>\n",
       "      <td>0.25</td>\n",
       "      <td>2.6875</td>\n",
       "      <td>5.125</td>\n",
       "      <td>7.5625</td>\n",
       "      <td>10.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nporosity</th>\n",
       "      <td>40.0</td>\n",
       "      <td>0.02225</td>\n",
       "      <td>0.992111</td>\n",
       "      <td>-2.08</td>\n",
       "      <td>-0.7575</td>\n",
       "      <td>0.140</td>\n",
       "      <td>0.7425</td>\n",
       "      <td>2.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count     mean       std   min     25%    50%     75%    max\n",
       "Depth       40.0  5.12500  2.922613  0.25  2.6875  5.125  7.5625  10.00\n",
       "Nporosity   40.0  0.02225  0.992111 -2.08 -0.7575  0.140  0.7425   2.35"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                                   # summary statistics "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we extract the X1 and X2 unit porosity samples from the DataFrame into separate arrays called 'X1' and 'X2' for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Npor = df['Nporosity']                                      # extract the 1D ndarrays from the DataFrame\n",
    "depth = df['Depth']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression Model\n",
    "\n",
    "Let's first calculate the linear regression model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model parameters are, slope (b1) = 0.14, and the intercept (b0) = -0.69\n"
     ]
    }
   ],
   "source": [
    "lin = LinearRegression()                                        # instantiate linear regression object, note no hyperparameters \n",
    "lin.fit(depth.values.reshape(-1, 1), Npor)                      # train linear regression\n",
    "slope = lin.coef_[0]\n",
    "intercept = lin.intercept_\n",
    "\n",
    "print('The model parameters are, slope (b1) = ' + str(round(slope,2)) + ', and the intercept (b0) = ' + str(round(intercept,2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the data and the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth_values = np.linspace(0.0,10.0,100)\n",
    "plt.subplot(111)\n",
    "plt.plot(depth, Npor, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.plot(depth_values, intercept + slope*depth_values, label='model', color = 'black')\n",
    "plt.title('NPorosity vs Depth')\n",
    "plt.xlabel('Z (m)')\n",
    "plt.ylabel('NPorosity')\n",
    "plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparison to a Non Parametric Model\n",
    "\n",
    "Let's run a quick decision tree for a comparision to a non parametric model.\n",
    "\n",
    "* we gain significant flexibility to fit any patterns from the data\n",
    "\n",
    "* requires more inference as nonparametric is actually parameter rich!\n",
    "\n",
    "This first model is a decision tree regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree                                    # tree program from scikit learn (packag\n",
    "depth_values = np.linspace(0.0,10.0,1000)\n",
    "my_tree = tree.DecisionTreeRegressor(min_samples_leaf=1, max_depth = 20)\n",
    "my_tree = my_tree.fit(depth.values.reshape(-1, 1), Npor)\n",
    "DT_Npor = my_tree.predict(depth_values.reshape(-1,1))\n",
    "plt.subplot(111)\n",
    "plt.plot(depth_values, DT_Npor, label='model', color = 'black')\n",
    "plt.plot(depth, Npor, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.title('Decision Tree Prediction of NPorosity from Depth')\n",
    "plt.xlabel('Depth (m)')\n",
    "plt.ylabel('NPorosity')\n",
    "plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and here is a random forest model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e0NNPPy1JOvXUU9WxY0dJ0quvvqrFixdr5MiRkqSdO3eqa9euHjyy2CCRBAAAAFoAny9fS0rL9lYkJWlJaaV8Pp93Qe2nKZXDWGnduvXer9PS0vZ+n5aWpurqarVq9e3UKdTttL6up845XXTRRZoxY0aMIvYWayQBAACAFmBi0WRNK6nSolUVqq5xWrSqQtNKqjSxaLLXoSWF448/Xo8++qgk6Z///Kc6d+6s9u3b73N8/vz52rJliyTppJNO0lNPPaX169dLkjZv3qzS0lJvgo8BKpIAAABACxBYBzlbM4tnyT/PL5/Pp6IpkxNmfWSiu/3223XJJZdo6NChys7O1sMPPyxJuu2223Teeedp+PDh+u53v6uDDjpIknTYYYfpzjvv1CmnnKLa2lplZGSouLhYffv29fJhRI3V7SqU6EaMGOEWLVrkdRgAAAAAomTFihUaNGiQ12G0ePX9HMxssXNuRH3jmdoKAAAAAIgIiSQAAAAAICIkkgAAAACAiJBIAgAAAAAiQiIJAAAAAIgIiSQAAAAAICIkkgAAAAAQJyeccIIOdEtDv9+vwYMHNzrmscceO6DzR4JEEgAAAEDSKFkwXxPGj9GoIfmaMH6MShbM9zqkhEIiCQAAAAB1lCyYr+Lpk3T9yDK9c12erh9ZpuLpk5qVTFZUVOjUU0/VsGHDNHjwYD3xxBOSpGnTpmnkyJEaPHiwLr/8cjnnJAUqitdee62OP/54DRo0SAsXLtSZZ56pAQMG6NZbb5UUSOYOPfRQXXTRRRo6dKjOPvtsVVZWfuvaL730ko455hgNHz5c55xzjsrLy781ZvHixRo2bJiOOeYYFRcX7z3u9/t13HHHafjw4Ro+fLjeeecdSdKNN96oN998UwUFBbrvvvsaHNdcJJIAAAAAksKc4lmaWpipEf1y1CrdNKJfjqYWZmpO8awDPueCBQvUs2dPLV26VB9//LHGjh0rSZo0aZIWLlyojz/+WDt37tTzzz+/9z6ZmZl64403dOWVV+qMM85QcXGxPv74Y/31r3/Vpk2bJEmffvqpLr/8cn344Ydq3769HnjggX2uu3HjRt1555165ZVX9P7772vEiBG69957vxXfJZdcot/+9rd699139znetWtXvfzyy3r//ff1xBNP6H/+538kSXfffbeOO+44LVmyRNdee22D45qLRBIAAABAUvD7V6ugb/Y+xwr6Zsvv9x/wOYcMGaJXXnlFN9xwg958803l5uZKkl5//XUdddRRGjJkiF577TUtW7Zs731OP/30vfc9/PDD1aNHD7Vu3Vr9+vXTl19+KUnq06ePjj32WEnSj3/8Y7311lv7XPe9997T8uXLdeyxx6qgoEAPP/ywSktL9xmzbds2bd26Vd/97nclSRdccMHe2/bs2aPLLrtMQ4YM0TnnnKPly5fX+/iaOi5SraJyFgAAAACIMZ8vX0tKyzSiX87eY0tKK+Xz+Q74nAMHDtTixYv14osv6qabbtIpp5yi66+/XldddZUWLVqkPn366Pbbb9euXbv23qd169aSpLS0tL1fh76vrq6WJJnZPtfZ/3vnnE4++WQ9/vjjDcbmnPvW/ULuu+8+devWTUuXLlVtba2ysrKaNS5SnlUkzayPmb1uZivMbJmZXeNVLAAAAAAS38SiyZpWUqVFqypUXeO0aFWFppVUaWLR5AM+59q1a5Wdna0f//jHuu666/T+++/vTRo7d+6s8vJyPfXUUxGf94svvtg7HfXxxx/X6NGj97n96KOP1ttvv63PP/9cklRZWamVK1fuM6ZDhw7Kzc3dW8189NFH9962bds29ejRQ2lpaXrkkUdUU1MjSWrXrp127NjR6Ljm8rIiWS3pF865982snaTFZvaycy46tVYAAAAAKaVw7DhJszWzeJb88/zy+XwqmjI5ePzAfPTRR5o8ebLS0tKUkZGhBx98UB06dNg7HdTn82nkyJERn3fQoEF6+OGHdcUVV2jAgAH66U9/us/tXbp00V//+ledd9552r17tyTpzjvv1MCBA/cZ95e//EWXXnqpsrOzVVhYuPf4VVddpbPOOkvz5s3TiSeeqJycQJV26NChatWqlYYNG6aLL764wXHNZaHuQ14zs39Imu2ce7mhMSNGjHAHuucKAAAAgMSzYsUKDRo0yOswosrv9+u0007Txx9/7HUoTVbfz8HMFjvnRtQ3PiGa7ZiZT9IRkv7tcSgAAAAAgEZ4nkiaWVtJf5P0M+fc9npuv9zMFpnZog0bNsQ/QAAAAACIgM/nS6pq5IHwNJE0swwFkshHnXNP1zfGOfeQc26Ec25Ely5d4hsgAAAAAOBbvOzaapLmSFrhnPv2zpsAAAAAWoRE6dvSUh3I8+9lRfJYSRdIGmNmS4L/fd/DeAAAAADEWVZWljZt2kQy6RHnnDZt2hTx/pKebf/hnHtLUv27awIAAABoEXr37q01a9aIfijeycrKUu/evSO6j5f7SAIAAABo4TIyMpSfn+91GIiQ511bAQAAAADJhUQSAAAAABAREkkAAAAAQERIJAEAAAAAESGRBAAAAABEhEQSAAAAABAREkkAAAAAQETYRxIAAABIEUVFRXr00UcbHdehQwfdeOON6ty5c4NjzEwnnnii8vLyohkiUgSJJAAAAJAi3nnnHeXl5en0008PO+7111/XT3/600bP9/Of/1y//vWvoxUeUgiJJAAAAJAidu/ereHDh+v+++8PO27Pnj1auXKlnHMNjhkzZoy2b98e5QiRKkgkAQAAgBRRVVWl1q1bNzouIyNDhx9+eNgx2dnZqqqqilZoSDE02wEAAABSRFVVlTIzM6NyrszMTO3Zsycq50LqIZEEAAAAUsTu3bubVJFsiszMTCqSaBCJJAAAAJAiolmRzMjIIJFEg0gkAQAAgBSxe/fuqE5tJZFEQ0gkAQAAgBTR1GY7TUEiiXBIJAEAAIAUUFNTo5qaGprtIC5IJAEAAIAUEKoeRqsiyRpJhEMiCQAAAKSAUNLHGknEA4kkAAAAkAJ2794tKXoVSRJJhEMiCQAJoGTBfE0YP0ajhuRrwvgxKlkw3+uQAABJJhYVSdZIoiEkkgDgsZIF81U8fZKuH1mmd67L0/Ujy1Q8fRLJJAAgIkxtRTyRSAKAx+YUz9LUwkyN6JejVummEf1yNLUwU3OKZ3kdGgAgiUR7aivNdhAOiSQAeMzvX62Cvtn7HCvomy2/3+9NQACApERFEvFEIgkAHvP58rWktHKfY0tKK+Xz+bwJCACQlGi2g3gikQQAj00smqxpJVVatKpC1TVOi1ZVaFpJlSYWTfY6NABAEqHZDuKpldcBAEBLVzh2nKTZmlk8S/55fvl8PhVNmRw8DgBA04QSSdZIIh5IJAEgARSOHUfiCABoltDU1mhWJGtra1VTU6P09PSonBOpg6mtAJCC2JcSAFqeWExtrXteoC4SSQBIMexLCQAtU7SntoYSSdZJoj4kkgCQYtiXEgBaplhMbZWoSKJ+JJIAkGLYlxIAWqZYNNupe954YGlG8iCRBIAUw76UANAyJXtFkqUZyYVEEgBSDPtSAkDLFKs1kvFKJFmakVzY/gMAUgz7UgJAyxSrrq3xarbj969Wwdl5+xwr6Jst/zx/XK6PyJBIAkAKYl9KAGh5oj21Nd5rJANLM8o0ol/O3mMszUhcTG0FAAAAUkBVVZXMTK1aRadWFO+prSzNSC4kkgCAuKIjHwDExu7du5WZmSkzi8r54p1IFo4dp6IpszVzYQ99554tmrmwh4qmzGaGTYJiaisAIG5CHfmmFmaq4Ow8LSkt07TpkyTxRgEAwtm9e7fuvvtubd++vcEx//znP6PWaEeK/xpJiaUZyYREEgAQN3U78kkKduSTZhbP4o0DAITx73//W7fffrvatGmj9PT0Bscde+yxUbtmvCuSSC4kkgCQINavX68ZM2bsbZbQEDPTueeeq+OPPz5OkUUPHfkA4MBs2bJFkvTWW29p+PDhcblmvJvtILmQSAJAgnj44Yd1//33q0uXLmHH7dy5Uw899JD69OkTdpzP59NLL70UtaYL0UBHPgA4MKFEskOHDnG7JhVJhJM47y4AoIV7/fXXdeihh2rFihVhx23btk133HGHNm7c2OCY9evXq6SkRO+9955Gjx4d7VAP2MSiyZo2fZKmFgYqkUtKKzWtpEpFU+jIBwDhbN26VZLUsWPHuF0zlEjOmzdPK1euDDv2uOOO03HHHRePsJAgSCQBIA7eeOMNvfbaa2HHvPnmm7rgggsaPVdubq7uvffesGO2bdumzp0767HHHlNeXl7YsTk5Oerbt2+j142GwDrI2ZpZPEv+eX75fD4VTZnM+kgAaEQokWzfvn3crtmtWzd17NhRTz75pJ588smwY4cOHaqlS5fGKTIkAhJJAIiDyZMn6z//+U/YMRkZGTrzzDOjcr3c3Fwdf/zxevDBB/Xggw82On7x4sVxW3NDRz4AiNzWrVvVvn37sI12oq1Dhw7asGGDampqwo4rKirSs88+G6eokChIJAEgDrZt26Zzzjmn0U90o+nhhx/WO++8E3bM+vXrdfXVV2vZsmVxSyQBAJHbunVrXNdHhqSnpzeavHbr1k0bN25UbW2t0tLYpr6lIJEEgDgoLy9Xu3bt4nrN3r17a8KECWHH7Nq1S1dffbX8fn98ggIAHBCvEsmm6Nq1q2pra7V582Z17tzZ63AQJ3xkAABxUF5errZt23odxrdkZWWpe/fuKi0t9ToUAEAYiZ5IStKGDRvCjnviiSeUmZm5t8rZ0H+ZmZl69NFH4xE6moGKJADEmHNOO3bsiHtFsqn69u1LRRIAEtyWLVuUn5/vdRj1Cm1btX79eg0aNKjBca+88oqys7N19dVXhz3f3LlzVVxcrPPPPz+qcSK6PE0kzezPkk6TtN45N9jLWAAgVnbt2qXa2tqErEhKgUTy/fff9zoMAEAYqVCRXL58uY444ghNnz497Lh27drphhtu0NChQ2VmDY47/vjj9bvf/S7ygBEVXlck/ypptqT/9TgOAIiZHTt2SFLCViR9Pp/+9re/6YQTTgg77pJLLtFFF10Un6A8UrJgvuYUz5Lfv1o+X74mFrE1CYDY27x5s6qrq8OO2bJlS8InkuvXr29wjHNOy5cv13nnndfo+S699FItXbpUlZWVDY756KOP9Oijj5JIesjTRNI594aZ+byMAQBirby8XJIStiJ51llnadGiRWHbu69du1aXXXaZPvroI7VqFf5PR79+/XTeeeeF7dyXnp6urKysA445FkoWzFfx9EmaWpipgrPztKS0TNOmT5I0m2QSQMw89thjTZ7CmaiNbDp16iRJWrFihVasWFHvmM2bN2vr1q067LDDGj1f586dG10jecstt+hXv/qVnHNhq5aIHa8rkgCQ8kKJZKJWJEeNGqVXX3017JiNGzfqxBNP1OzZs8OOc86pqqpKV1xxRdhxaWlpeumll3TSSSdFHG+szCmepamFmRrRL0eSNKJfjqYWSjOLZ5FIAoiZJUuWKDMzU/fdd1/Ycenp6TrrrLPiFFVkWrVqpR49emj27NmN/p0YMmRIVK6Zm5urmpoaVVRUJOwHtaku4RNJM7tc0uWSdNBBB3kcDQBELjS1NZn/0HXu3FkfffRRo+Occ1qwYIE+/vjjsOOmTZump556KqESSb9/tQrOztvnWEHfbPnn+b0JCECLsGbNGvXp00dXXXVVg2NC0+7nzL47Yafdz58/X5988knYMW3bttXxxx8flevl5uZKkrZv357Uf1+TWcInks65hyQ9JEkjRoxwHocDABFL9IpkNJmZxo0bp3Hjwr/Befvtt/Xiiy/qtddea/R8Rx11lLKzs6MZZr18vnwtKS3bW5GUpCWllfL5fDG/NoCWa82aNerdu3eDtyfLtPthw4Zp2LBhcbteKJHctm2bevbsGbfr4hsJn0gCQLJLhYpktI0fP17/+Mc/mlSRvPnmm3XXXXfFPKaJRZM1bfokTS0MVCKXlFZqWkmViqZMjvm1AbRca9as0Xe+850Gb2faff3qJpLwhtfbfzwu6QRJnc1sjaTbnHNzvIwJAKIt0ZvteOHiiy/W4YcfrqqqqrDjzjzzzLBdAKMp8IZstmYWz5J/nl8+n09FUxJv+hiA1FFbW6uvvvpKvXr1anAM0+7r1759e0kkkl7yumtr4/1/ASABRbJNREua2tpU6enpOvrooxsd16FDB1VUVMQhooDCseNIHAHEzcaNG1VVVRV2aivT7utHRdJ7TG0FgAhFul6Fqa0HLicnJ66JJABE0xVXXBF2LXhoVka4iiTT7utHIuk9EkkAiFDd9So7d+7UgE7V+vmxVZo+7UbV1H67J9jSpUvVqlUrtW7d2oNokxuJJIBkFJq18q9//VNZWW3U//BR6tFAQ5icnByNGTOmwXMx7b5+dbu2whskkgAQodB6FecCmy/X1jpl1UoffrhGp556ar336dOnDxsmH4CcnJy9U4MBIBmEZq3cekqGfp7vtFU5emDxF7rwgusPOPlj2v23tW3bVmZGRdJDJJIAEKHQepXhvjaqrXXq2rWLSsuzdeSRnTVt1gP13qdPnz5xjjJ6IlkPGm05OTlat25dXK4FANEQmrUypFeGPtoijfBla2rnzBbfZTXa0tLS1L59exJJD5FIAkCEQutVbv5erVQrrVifpuKF6bp2ynSNGjXK6/Ciyuv9y9q2bcvUVgBJJTRrZdfOSklSRkYGXVZjpH379lq2bJmee+65sOO6dOnSpAZviAyJJABEKLReZca9d+qdd1Zq4MBcTf3l/Sn5SbPX+5exRhJAsgnNWumft0eS1Cojgy6rMdKnTx+9+uqrevXVVxsd6/f71bdv3zhE1XKQSALAASgcO04DBh6i/v37655rbk7JJFLyfv8yEkkAySY0a+XqoyrUrlb6cM0ezXi1tsV3WY2F559/XqtWrQo7Zvny5brwwgv14YcfkkhGGYkkABygXbt2SZKysrI8jiR2vN6/LJRIOudoVgQgKYRmrdx83VXy+6UTTuitoikH3mgHDevYsaOOPPLIsGMOPvhgSdKyZcs0fvz4eITVYpBIAsABCiWSbdq08TiS2PF6/7KcnBw557Rz505lZ2fH5ZoA0JgFCxbo66+/Djsmt3t/aV255j3/epyiQn1yc3PVu3dvLVu2rNnn8rL5XCIikQSAA9QSKpJe71+WkxOohFZUVJBIAkgI69ev17hxTfs3cPTo0TGOBk1x+OGH6+WXX9all17a4Bgz05VXXqmRI0fWe7vXzecSEYkkAByglpBISt7uX1Y3kezSpYsnMQBAXZs3b5Yk/eY3v9Hpp58edmz37t3jEZKnkqFKd84552jFihV65ZVXGhyzYcMGrVq1Sq+/Xn8F2evmc4mIRBIADtDOnTslpX4i6aW6iSQAJILy8nJJUn5+fovvxJosVbqJEydq4sSJYcfcdddduvXWW3XllVeqVatvp0iLFy9S3qjW+uKLTWrfPlcdOuS2+G1dSCQB4AC1lIqkl0gkASSaUCLZtm1bjyPxXqJX6SKpll566aX6y1/+oqeeeqre2zOqd+rdTyt0aFenbdu2q0OH3Ba/rUua1wEAQLIikYw9EkkAiYZE8ht+/2oV9N13/XpB32z5/X5vAqojVC29fmSZ3rkuT9ePLFPx9EkqWTC/3vE9evTQ559/ro0bN9b731//71k9sdqnDdWdVbFzt977bIemlVRpYlHL3daFiiQAHKCW0LXVa6E3al9//fXedUkNjcvMzGzSOdeuXavq6uqwY3r37q20ND5rBfBtJJLf8HqLqHCiXS0NNZ+bNf0mLV26QcOGtdHkKTMSovLqFRJJADhAVCRjLzc3V5L0ox/9KOy4Xr166d///rd69eoVdtwNN9ygmTNnNnrdU089VX//+98bTSbT0tLY3xJoYUgkv+H1FlHh+P2rVXB23j7HmrumsXDsOI066mjl5eXpiw2V+uWMu/XLGXfXOzY3N1dz585V+/btD/h6iY5EEgAOEIlk7A0YMEBz587Vpk2bGhyzZ88eTZ06VQMGDGj0Z7FlyxZNmDBBhYWFDY5ZtmyZ7r333iZVOE8++WS99NJLjY4DkDpIJL/h9RZR4cSqWtqxY0dde+21+uCDDxocs3XrVr3xxhv68MMPU3oLGBJJADhAdG2NPTPT+eef3+i4I444Qs8880yj4zp16qTrrrtu79rL+jjnNGzYMJWWloY910svvaR333230WsCSC2hRDLcvyMtiZdbRIUTy2rpvffeG/b2hQsXatSoUdq6dWuzr5XISCQB4ADt2rVLaWlp9bYJR3yNGTNGY8aMicq5zEwXXnhho+OysrL01ltvaceOHWrXrl1Urg0g8ZWXlyszM7PJ67LhDS+rpaFlGdu2bYv5tbzEux8AOEC7du1SVlYWa+RaqJ49e0oKNO855JBDPI4GQLyUl5czrTVJeFUt7dChgySlfEWSlnQAcIB27dpFx9YWrEePHpKksrIyjyMBEE/l5eXMQkBYoYokiSQAoF6hiiRaproVSQAtBxVJNKZ169bKyspK+amtJJIAcIBIJFu2piSSJQvma8L4MRo1JF8Txo9pcCNsAMmDRBJNkZubm/IVSdZIAsAB2rlzJ4lkC9auXTtlZ2dr6dKlev/99791+zvvvK3n/vorTSlsraPOztPS0jJNmz5J0uyE7HAItHQlC+ZrTvEs+f2r5fPla2JR/Y1ZSCTRFB06dEj5iiSJJAAcICqSLZuZKT8/X3PnztXcuXO/dXunbOne06WsCqmyfIBG9GuvqYXSzOJZJJJAgilZMF/F0ydpamGmCs7O05IwH/yUl5erc+fO3gSKpEFFMsjMJkl61Dm3JcbxACmpqZ9yIrmQSOIf//iHli1bVu9tN197mcYd1Val/lWqqKhQbm57FfTNln+eP75BAmjUnOJZmlqYqUHdTF984VeepEsHV+vnV12sYUeftM/YVatWafDgwd4EiqRBRfIb3SUtNLP3Jf1ZUolzzsUuLCB1RPIpJxLHp59+qttuu0179uxpcMzSpUt1xBFHxDEqJJr+/furf//+9d4294/3q3R7mVq3bq2dO3dKkpaUVsrn88UxQgBN4fevVsHZeSpbu0abN21W66zW6pvrtGVzpRYtWrTP2O7du+uUU07xKFIki9zcXJWWlnodRkw1KZF0zt1qZlMknSLpEkmzzexJSXOcc/+NZYBAsgt9yjmiX44kaUS/HKa3JYEXX3xRTzzxhAYNGqT09PR6x/To0UPjx4+Pc2RIFhOLJmva9Em65PAM5bsKLVpVoWklVSqaMtnr0ADsx+fL15LSMnVOr1VGRisNGTxYi1ZVaPRxPfTkc695HR6SUIcOHZjaGuKcc2b2taSvJVVL6ijpKTN72Tl3fawCBJJd6FPOupjelviqq6slSQsXLlROTo7H0SAZBT4omq1br7tKfr9fOW3XqFVOF91408268aabvzX+/PPP13XXXRf/QAHs/eDn0sFV6tve+OAHzZabm6sNGzboqKOOanTsL37xC02YMCEOUUVXU9dI/o+kiyRtlPQnSZOdc3vMLE3SZ5JIJIEGhD7lDFUkJaa3JYNQItlQNRJoisKx4+TLX6Bbbrml0WnSDzzwAIkk4JHQBz/XXHmhdmzfqWOP7aGiKfQzwIE766yztGLFCtXU1DQ6Nln7LTS1ItlZ0pnOuX0m+jrnas3stOiHBaSO0KecUwsDlcglpZXN+pSTxj3xEfqHv1UrmlujeQ455BA99dRTYcfMnDlTN9xwgzZt2qROnTrFKTIAdRWOHac+AwpUXl7OdFY029FHH63nn3/e6zBiKq2J4/L3TyLN7BFJcs6tiHpUQAopHDtORVNma+bCHvrOPVs0c2EPFU05sEY7ocY9148s0zvX5en6kWUqnj6JTc5jgIok4unII4+UpHr3o6yrZMF8TRg/RqOG5GvC+DH87gNRVlFRwXIGoIma+lH74XW/MbN0SUdGPxwgNRWOHReVqiGNe+Knurpa6enpMjOvQ0ELMHz4cEnS2Wefrezs7HrHVO3epd5tdmjquCydcfYhdIAGYqCyslJdunTxOgwgKYRNJM3sJkk3S2pjZttDhyVVSXooxrEB2A+Ne+KnpqaGaiTipmPHjrr//vu1fPnyBse8+9rzmnL8bvVtWyFXW80HSUAMUJEEmi5sIumcmyFphpnNcM7dFKeYADSAxj3xU11dzfpIxNU111wT9vZRQ/I15ojeWvX5Z6qs3Knc3Aw+SAKijEQSaLqwayTN7NDgl/PMbPj+/8UhPgB1TCyarGklVVq0qkLVNW5ve/KJRbQnjzYSSSQany9fKzcEvt65s1ISHyQB0UYiCTRdY++Sfi7pckm/ruc2J2lM1CMC0KBQe/KZxbPkn+eXz+ejPXmMMLUViWZi0WT9cvokndc/Xa0yN+o/q8o145Vadezv0+WXX77P2IyMDN10003q3bu3R9ECycc5RyIJRKCxqa2XB/9/YnzCAdCYaDXuQXhUJJNHS9kSJ/RB0s0/v1JfffWV0tN3apflqHX5Ui1ZunTvOOecvv76aw0YMEA/+9nPPIsXSDZVVVWqqakhkQSaqEnvkszsHEkLnHM7zOxWScMlTXfOfRDT6ADAIySSySG0Jc7UwkwVnJ2X8p1MAx8klYYd45xTVlaW1q5dG6eogNRQUVEhSSSSiJtk/yC0qftITgkmkaMlFUp6WNLvYxcWAHgrtP0HElvdLXFapVuwk2mm5hTP8jo0z5iZevbsSSIJRIhEEvGUCnuDNzWRrAn+/1RJDzrn/iEpMzYhAYD3ampqqEgmAb9/tQr67rvvYkHfbPn9fm8CShAkkkDkSCQRT6nwQWhTE8mvzOwPkiZIetHMWkdwXwBIOkxtTQ6BLXEq9zlGJ1MSSeBAkEginlLhg9CmJoMTJJVIGuuc2yopTxL7DQBIWUxtTQ5siVO/Xr16kUgCESKRRDylwgehTfq43TlXaWb/lVRoZoWS3nTOvRTb0ADAO0xtTQ5siVO/nj17aseOHdqxY4fatWvndTiA50pLS7Vw4cKwY5YsWSKJRBLxMbFosqZNn6SphYFK5JLSSk0rqVLRlOT5ILSpXVuvkXSZpKeDh+aa2UPOud/FLDIgxVRXV2vnzp2Njlu7dq1KS8N3ZUxPT9fo0aPVunXraIWH/TC1NXmwJc639ezZU5I0aNCgRl/Ht9xyiy677LJ4hAV45rLLLtPLL7/cpLE9evSIcTRAanwQas65xgeZfSjpGOdcRfD7HEnvOueGxji+fYwYMcItWrQonpcEoqKmpkaHHXaYVq5cGbVzTp06VXfccUfUzod9nXbaaSorK9PixYu9DgWI2MaNGzVlypRGP7x64YUXdMwxx+jZZ5+NU2SAN4466ihlZGTo978Pv+lAbm6u+vTpE6eogMRnZoudcyPqu62pH7ebvuncquDX1tzAgJbizTff1MqVK3XFFVdowIABYcfm5uZq0KBBMmv4V+z222/XAw88oLZt2zZ67dNOO02DBg2KOOaWjqmtSGadO3fWgw8+2Oi4008/XV988UUcIgK8VVVVpe7du2vw4MFehwKkjKa+S/qzpH+b2d+D3/9A0pyYRAQkmc8//1zXXnutdu/e3eCY1atXKzs7W7/+9a+jsvZi6tSpOuGEE3T99dc3Ovadd97R3//+90bHYV9MbUWqK1kwX198+r7Kyso0YfyYpNsIG4hEVVWVMjPZuQ6IpkbfJZlZmqR/S/qXpNEKVCIvcc59EOPYgKSwYMECPf/88xo1alSDXT67dOmin/zkJ1FbwD969GhVVFSouro67Lgf/vCH+uyzz6JyzZaGrq1IZaGNsO8eW6UOrlaWt1Z3TZ8kaTbJJFISiSQQfY0mks65WjP7tXPuGEnvxyEmIKl8/fXXSk9P1zvvvBPXxKN169aNNts55JBD9Morr6i2tlZpaWz9GgmmtiKVhTbC7tehrVat2qDDe6VraqFpZvEsEkmkpN27d5NIAlHW1HdJL5nZWZKedk3pztNEZjZW0m8kpUv6k3Pu7midG4iXsrIyde3aNSGrV/3799euXbtUVlamXr16eR1OUqmurlZWVpbXYQAx4fevVsHZedq1M/AnfUd5uQZ2zdHKlSv10UcffWt8ly5d1L1793iHCURNVVUVnc6BKGtqIvlzSTmSasxsV/CYc861P9ALm1m6pGJJJ0taI2mhmT3rnFt+oOcEvPD1118nbKvwgw8+WFJgHSeJZGSY2opUFtgIu0zD+gTeWH9R+oWWr5PWrJGGDv12Q/Y2bdpo48aNys7OjneoQFQwtRWIviYlks65WOxmPErS5865VZJkZv8n6QxJJJJIKmVlZQmbSPbv31+SNHHiROXl5dU7Ztu2rbLdW9SubVvl5+fTcCOIZjtIZXU3wu4/YKA+8Ffo4eXV+um1E1VwxPB9xr7xxhv67W9/q7Kysr3/pgDJhkQSiL4mv0sys9MlHR/89p/Oueebee1ekr6s8/0aSUc185xA3H399dcaPnx44wM94PP5dMUVVzTY3n/jhvXK2VWqn32nSmee0E2frCvTNBpuSGKNJFLbPhth+/3y+Qbq+l/V/yFSTk6Ofvvb32rdunUkkkhau3fvZmorEGVNepdkZndLGinp0eCha8xstHPuxmZcu75N8r61/tLMLpd0uSQddNBBzbgcEH01NTVat25dwq4dSktLC7v58oTxY3TViV3VtmqN5Go0ol+OphaKhhtiaitSX+HYcU36Pe/ataskaf369bEOCYiJ2tpaVVdXU5EEoqypH7d/X1KBc65WkszsYUkfSGpOIrlGUp863/eWtHb/Qc65hyQ9JEkjRoyIWqMfoDGfffaZnn76aYXrL1VZWana2tqEndraGL9/tY4Y11afrZSqq/dIkgr6Zss/z+9tYAmAqa1AQLdu3SRJ69at8zgS4MDs2RP4+0YiCURXJO+SOkjaHPw6NwrXXihpgJnlS/pK0g8l/SgK5wWi4rbbbtPjjz/e6LhWrVpp2LBhcYgo+ny+fC37+gtlStoT3JNySWmlfD6fp3ElAqa2AgFdunSRREUSyauqqkoSiSQQbU19lzRD0gdm9roCU1KPl3RTcy7snKs2s0mSShTY/uPPzrllzTknEE2LFy/W6aefrieeeCLsuLS0tKT94zSxaLJm3FGkc3xSt+57tGhVhaaVVKloymSvQ/McU1uBgMzMTHXs2JGKJJLW7t27JYk1kkCUNTWRfFnSvySNUCCRvME593VzL+6ce1HSi809DxBt27dv18qVK3XBBRek9F6CgfVRxbpwwmlq969yDT/yEBVNoWurxNRWoK5u3bqRSCJpUZEEYiPsuyQzGy/pz5KqJdVIOtc593Y8AgNiae7cuVq9enWDt69dG1iue+SRR8YrJM8Ujh2n7E4H6ZjRo/XII494HU7CYGor8I2uXbvq888/1z//+c+w47KzszVy5EiZ1ddPzzslC+ZrTvEs+f2r5fOxzVFLQyIJxEZj75LuknScc+4TMztK0kxJ3419WEDsbN68WRdccEGj4zp16qSjjmoZO9J07dqV9U/7YWor8I38/Hw9/PDDOvHEExsd+8orr+ikk06KQ1RNU7JgvoqnT9LUwkwVnJ2nJaVsc9TShKa2kkgC0dVYIlntnPtEkpxz/zazdnGICYiplStXSpKeeeYZnXbaaQ2OMzOlpaXFKyxPdenSRcuXL9fcuXPDjmvTpo1OP/10ZWRkxCky7zC1FfjGb37zG1188cVhx+zatUvjxo3TwoULEyqRnFM8S1MLMzWiX44ksc1RCxSqSLJGEoiuxt4ldTWznzf0vXPu3tiEBcROKJE89NBDqTgFDRw4UC+88EKTKrUvvPCCvv/978chKm8xtRX4Rm5urk444YRGx/Xt21dLly6NfUAR8PtXq+DsvH2Osc1Ry8LUViA2GnuX9EdJ7cJ8DySdzz77TGlpacrPz/c6lIQxa9YsXXXVVWHHrFq1SoWFhdq2bVucovIWU1uByA0bNizhEkmfL19LSsv2ViQltjlqaUgkgdgIm0g65+6IVyBANGzevFmVlZVhx3z00Ufy+Xz8QakjPT1dBx98cNgxoemsO3fujEdInmNqKxC5YcOG6dlnn1W7duE/c77iiit0zz33xCWmiUWTNW36JE0tDFQil5RWss1RC8MaSSA2GuvaOjXMzc45Nz3K8QAHpGTBfP125u36z3/+I+ekTeFzSZ166qnxCSyFhLZB2bVrl8eRxAeJJBC5yy67TLt371Z1dXWDY9544w396U9/0owZM+Ky3jqwDnK2ZhbPkn+eXz6fj22OWhjWSAKx0di7pIp6juVImiipkyQSSXgu1JHvumN3q81gabM6aebrpoKTfqTBg4fUe5+mdB7Evtq0aSOpZVQka2tr5ZxjaisQgaZusfH3v/9dZ555pu67775GZ0IMHjxYAwcObHZshWPHkTi2YExtBWKjsamtvw59HezYeo2kSyT9n6RfN3Q/IJ5CHfl87Wvl90snDemhrp2rNXPhR7r//t94HV7KaEkVyZqaGkmiIgk0USRbbJx88slq166dbrjhhkbPm5WVpeXLl7OmHc1CIgnERqPvkswsT9LPJZ0v6WFJw51zW2IdGNBUoY58mzaWSwqs9yvom0lHvijLyMhQWlpai6hIhqblkUgCTRPJFhtt27bVypUrG927dvv27SosLFT//v3DbsWUmZmp5557LqG2HEFiCa2RZGorEF2NrZGcJelMSQ9JGuKcK49LVEAEQh35emQFqkjp6a20eHUFHfmizMyUlZXVoiqSTG0FmibSLTa6d++u7t27N3reBQsWqKSkJOyY2bNn63//939JJNEgKpJAbDT2cfsvJO2WdKukW8wsdNwUaLbTPoaxAU0S6sh3+bBd6tY6TYtXV9CRL0ZaSiJJRRKITKy22DjuuON03HHHhR3zxRdf6Lnnnmu0C6yZ6cwzz2SabAtEIgnERmNrJBueSwIkiFBHvl8UXaJNm0zHHdeDjnwx0qZNG6a2AvgWL7fY+PGPf6y5c+dq8uTGr7Vy5Ur94Q9/iHlMSCwkkkBs8C4JKaFw7DjNPmykMtas0ZPPvdbs8zW1+2BL01IqkkxtBSLj5RYbp5xyiioqKvb+3jbkO9/5jsrKymIeDxIPaySB2CCRRMrYunWrOnTo0OzzRNJ9sKWhIgmgIV5usRHaniic7t27N9rgB6mJiiQQG0xdRcrYsmWLOnbs2Ozz1O0+2Crdgt0HMzWneFYUokxuLaUiSSIJpJ6uXbuSSLZQoUQyIyPD40iA1MK7JKSMaCWSkXYfbElaSkWSqa2At2KxvIBEMnWtWbNG27dvb/D2tWvXKj09nX/TgSgjkUTKiNbU1lh1H0wFWVlZ2rFjh9dhxBwVScA7sVpe0LVrV1VUVKiiokI5OTmN3wFJYdWqVerfv3+j46LxQTOAffEuCUnh1ltv1YwZM8KOqa2tVV5eXtgxTeFl98FEl5WVpQ0bNngdRsyRSALeqbu8QFJweYE0s3hWsxNJSdqwYQOJZAr58ssvJUlTp07V4Ycf3uC4gw8+OF4hAS0G75KQFD744AN17dpVP/nJTxoc06pVK1188cXNvpaX3QcTXUuZ2hpKJJkGBcRfrJYXhBLJdevWMcMkhYRmyZx22mkaOXKkx9EALQuJJJJCVVWV8vPzNX369Lhcz8vug4mspTTbCa2RpCIJxF+slhd069ZNkvTkk0/qk08+CXN9n7773e8261qIn9DayPbt23scCdDy8C4JSWH37t207U4AqVKRbOwxVFRUSCKRBLwQq+UFPp9PmZmZuvfee8OOa9WqlSorK+nwmSRCFcl27dp5HAnQ8vAuCUmhqqpKubm5XofR4qVCRfLmm29udL1tCJtXA/EXq+UFXbp0UVlZmbZt29bgmD/+8Y+aMWOGdu3aRSKZJKhIAt4hkURSqKqqoiKZAFKhIrl8+XL16NFD11xzTdhxOTk5Ov744+MUFYC6YrW8IC8vL2xTtu7du0v6Zt9BJL4dO3bIzGigBHiARBJJYffu3VSHEkBWVpb27NmjmpqapG1EU1lZqb59++qGG27wOhQACSb0gSWJZPLYvn272rVrJzPzOhSgxUnzOgCgKahIJoY2bdpIUlJPb925c6eys7O9DgNAAiKRTD47duxgfSTgESqSSApUJBNDVlaWJOmWW27Zm1TWZ9y4cQk7LbSyspL1tgDqRSKZfLZv3876SMAjJJJIClQkE8PgwYPVtm1bPfjggw2O2bNnj95++2298cYbcYys6Xbu3Bk2CQbQcpFIJh8qkoB3SCSRFKhIJoYxY8bsbbXekLPOOksrV66MU0SRq6ysZGorgHqF/s6QSCYPKpKAd1gjiaRARTJ5lJfv0OavPtOoIfmaMH6MShbM9zqkfVCRBNAQKpLJh4ok4B0SSSQFKpLJoWTBfJX7/6NfjavSO9fl6fqRZSqePimhkkkqkgAaEkokd+/e7XEkaCoqkoB3mNqKhFdTU6OamhoqkklgTvEs3VqYoS5pTulp0oh+OZpaKM0snhWTPeEi5ZyjIgmgQVQkE8fOnTs1f/587dmzp8ExS5d8oF1bvtKbLz2tCeO/0MSiyQnxtwZoKUgkkfBCf9CpSCY+v3+1hh6XpbK1gQ8AWrVqpYK+2fLP83sdmiTt3QOTiiSA+pBIJo6HH35YP/3pT8OOOaybNOtUaUxBO329u0zTpk+SNJtkEogTEkkkvNAfdCqSic/ny9eKdZ+og75JJJeUVsrn83kcWcDOnTsliYokgHqRSCaO1atXKzMzUx988IHM7Fu3X1d0sW46ZqNG5LdR69ZZ6m1KqBkwQEtAIomEF1qrQkUy8U0smqwZN16iiwZJAw6p1kdfVWtaSZWKpkz2OjRJgfWRkqhIAqgXXVsTx1dffaWePXvqsMMOq/f2DRvW6+iBeWqV/k2SmUgzYICWgGY7SHhUJJNH4dhxKvzRtfr5s9Jxv96qmQt7qGhK4kwzoiIJIBwqkonjq6++Uq9evRq83efL15LSyn2OJdIMGKAlIJFEwmONZHI55ZRCbaqU7pj1gJ587rWESSIlKpIAwvMikSxZMF8Txo9J2C2TvNJYIjmxaLKmlVRp0aoKVdc4LVpVoWklVZpYlBgzYICWgKmtSHihqa1UJJNDhw4dJElbt271NI76UJEEEE68E8mSBfNVPH2SphZmquDsPC0ppWGMFOiw/dVXX+m0005rcEzg+ZmtmcWz5J/nl8/nU9EUurYC8UQiiYRHRTK5hBLJbdu2eRtIPahIAggn3vtIzimepamFmRrRL0dSdLZMKlkwX3OKZ8nvXy2fLz8pt8TYtm2bKisrw1YkpeByiiR7bEAqIZFEwqMimVzatWsnSXrggQdUUlISduzQoUP1q1/9Kh5hSaIiCSC8eFck/f7VKjg7b59jzWkYkywVziuuuEIvv/xyg7eH9o5sLJEE4C0SSSQ8mu0kl/T0dF122WVaunSpNm/e3OC4r776SiUlJZoxY4bS0uKzXJuKJIBw4t21NdAwpmxvRVJqXsOYWFQ4o628vFxz5sxRQUFBgx1ZpcAHfieffHIcIwMQKRJJJDy2/0g+Dz30UKNjfvOb3+hnP/uZtm3bpo4dO8YhKiqSAMJr1SrwtiheieTEosmaNn2SphYGKpFLSiubtWVS3QpnrXOSk4b2aaPVT67Srl27vjU+IyND6enpzXoMkXr77bdVU1OjGTNmkCgCSY5EEgmPimRqyssLvNnZvHlz3BJJKpIAwjEzZWZmxi2RjLRhzAcffKCf/exne/8t29/GjZv09L/KNLh7mnbu3CnnpOXrpNWr6/8ArUePHrrtttsa/DfRzPTh0qV6758vaOPGDercuYuOPuFUDSso+NbYPXv26IUXXtCWLVvCPuYvvvhCGRkZ+s53vhN2HIDERyKJhEdFMjWFksfNmzerf//+zT5fVVWVxo0bp7Vr1zY4JvQGh4okgIbEM5GUvmkY8+677+q9997T8hWfaPmKT+odO2fOHK1bt06jRo2q9/Y0k2b9c5luOilNR/Ttqk/XO/1pabVOP/cMDRx4yD5jnXP6y1/+oiuvvDJsfId1k244URp4jLRyw0b96pEVmnVP/WO7du2qAQMGhD1f165ddc455ygnJyfsOACJj0QSCY+KZGqqW5GMhlWrVum1117TMcccoz59+jQ4rm/fvnuvDQD7i3ciGfKjH/1Ifr8/7JjMzEw9+eSTOuOMMxocE+raevd7gQrnLfc2XOH8xS9+oS+//LLe25xzuvbKC3Tj0Rt0pC/w4VuBpP79d2rGu5113+8f+db4gw46SFlZWWEfA4DUQSKJhEdFMjWFKpKNTYNqqrKyMknSnXfeqTFjxkTlnABaHi8SyfXr18vv9+uuu+5SUVFRg+MyMjIanZofyZYYmZmZYWeErFv3tY4akKdW6bb32FEDMrX+H+sbrTwCSH3xaZUINAMVydQU7YpkKJHs0aNHVM4HoGVq3bp13PaRDFm4cKEkafTo0crNzW3wv3iv7w50ld13PWZzusqWLJivCePHaNSQfE0YP0YlC+ZHIUoAXqEiiYQXSiSpSKaWumsko4FEEkA0RLMi6ZzTddddp1WrVoUd99///ldpaWkaPnx4VK4bLdHsKpsse1wCaDoSSSS80CfDVCRTS2ZmpnJycqI6tTUrK0u5ublROR+AlimaieSWLVt07733qmfPnurcuXOD48xMV1xxhdq2bRuV60ZLpF1lw0mGPS4BRIZEEp6aMGGC/vWvf4UdU1FRIYmKZCrKy8uLakWyR48eMrPGBwNAA6KZSIb+fZsxY4YuvPDCqJwz3iJZcxlO3T0uQwr6Zss/z9/scwPwhieJpJmdI+l2SYMkjXLOLfIiDnjvpZde0kEHHaRjjz027Lj+/fuz918K6tixo5YtW6YnnniiwTGtWrXSKaeconbt2oU9V1lZmXr27BntEAG0MNGuSEqiU7RC6y3L9lYkpeattwTgPa8qkh9LOlPSHzy6PhKAc04VFRUaP3687rrrLq/DgQf69eunZ555Rj/84Q/DjuvUqZO6d+8edsznn3+u8ePHRzM8AC1QLCqSoTXhLVk011sCSAyeJJLOuRWSmILWwlVVVam6uppNiVuwxx57rNF909asWaO//vWvjXZRPPTQQ3X55ZdHMToALVFmZqZ27doVlXOFEkkqktFdbwkgMbBGEp4pLy+XpIRrLoD4adOmjQYNGhR2zKBBg3TyySdH5Xqhjbr9/tXy+fI1sYg3MQD21bp1a33wwQf60Y9+FHZc27Ztdc8996h9+/YNjmFq676itd4SQGKIWSJpZq9Iqm8u2i3OuX9EcJ7LJV0uSQcddFCUokMiCDXRIZFEPNB6HkBTjB07Vp999pkWLWq4fUNVVZVKS0t16qmn6owzzmhwHFNbAaSymCWSzrnvRek8D0l6SJJGjBjhonFOJIZQRZKprYgHWs8DaIqrr75aV199ddgxW7ZsUV5enj777LNGx+Xk5LB9FYCUlOZ1AGi5qEginvz+1Srou2/n34K+2Y2u0QSA/XXs2FGdOnVqNJHcvHkz1UgAKcuTRNLM/p+ZrZF0jKQXzKzEizjgLSqSiKdA6/nKfY7Reh7AgRowYIBWrlwZdkyocgkAqcirrq1/l/R3L66NxEGzHcQTrecBRNOAAQP08ssv6+23325wTGlpKRVJACmLrq3wDFNbEU+0ngcQTUOGDNEjjzyi0aNHhx3XWPdXAEhWJJLwzHvvvatO2dKP/t8pOvjgg9mKATFH63kA0XL11VfryCOPVE1NTdhxRx55ZJwiAoD4IpGEJ0oWzNcHLz+qe0+XJnwvTx9/xVYMAIDkkZWVpTFjxngdBgB4hq6t8MSc4lm66STTYd2kzMz04FYMmZpTPMvr0AAAAAA0gkQSnvD7V+vQ7ukyM6WZSWIrBgAAACBZkEjCEz5fvj5eU6W09G9egmzFAAAAACQHEkl4YmLRZN35crU+WS9V1zgtWlWhaSVVmljEVgwAAABAoqPZDmJiy5Ytuueee7Rr164Gx3y1q71uWFCj+z7YwlYMqFfJgvmaUzxLfv9q+Xz5dPYFAABIECSSiIkFCxbol7/8pbKzs5WW1nDh+9xzz9Of/vSnOEaGZFGyYL6Kp0/S1MJMFZydpyWldPYFAABIFCSSiIny8nJJ0qeffqrevXt7HA2S0ZziWZpamKkR/XIkKdjZV5pZPItEEgAAwGOskURMVFRUSJJycnI8jgTJyu9frYK+2fsco7MvAABAYiCRREyQSKK5fL58LSmt3OcYnX0BAAASA4kkYqKiokLp6enKyMjwOhQkqYlFkzWtpEqLVlXQ2RcAACDBsEYSMVFRUaGcnByZmdehIEkF1kHO1sziWfLP89PZFwAAIIGQSCaYVNnuoLKykmmtaLbCseOS8vUPAACQ6pjamkBC2x1cP7JM71yXp+tHlql4+iSVLJjvdWgRC1UkAQAAAKQeEskEUne7g1bpFtzuIFNzimd5HVrESCQBAACA1EUimUBSabsDEkkAAAAgdZFIJpBU2u6goqJC2dnZjQ8EACDJlCyYrwnjx2jUkHxNGD8mKZegAEBzkUgmkFTa7oBmOwCAVJRK/QwAoDlIJBNI4dhxKpoyWzMX9tB37tmimQt7qGjK7KTsWsnUVgBAKkqlfgYA0Bxs/5FgUmW7AxJJAEAq8vtXq+DsvH2OFfTNln+e35uAAMAjVCQREySSAIBUlEr9DACgOUgkEXXOOZrtAABSUir1MwCA5mBqK6KuqqpKtbW1VCQBACknsPxktmYWz5J/nl8+n09FUyanxLIUAIgEiSSirqKiQpJIJAEAKSlV+hkAQHOQSEbBRx99pE8//TTu1+3Vq5eOOeaYuF7z8ccf14033ijnXINjampqJJFIAgAAAKmKRDIKHnvsMd19991xv25aWpq2bNmi9u3bx+2aL7/8sjZv3qxzzjkn7LjWrVvr+9//fpyiAgAAABBPJJJRcM011+j888+P6zWffvpp3Xbbbdq2bVtcE8l169Zp4MCB+vOf/xy3awIAAABILCSSUdC9e3d17949rtdctmyZJGnHjh1xve769evVrVu3uF4TAAAAQGJh+48k1bZtW0lSeXl5XK+7bt06de3aNa7XBAAAAJBYSCSTlBeJ5IL5L2rXljV66+VnNGH8GJUsmB+3awMAAABIHExtTVLt2rWTFL+prSUL5mv2HUW65zSnMUe01de7yjRt+iRJs2mBDgAAALQwVCSTVLwrknOKZ+mm76XrsG5Sm9aZGtEvR1MLMzWneFZcrg8AAAAgcZBIJql4J5J+/2od1iNdktQqI1DILuibLb/fH5frAwAAAEgcTG1NUtFOJJ1zOu6447RixYp6b8+s2a4X3qnRoV2kjFaBl82S0kr5fL6oXB8AAABA8iCRTFI5OTmSordGsry8XG+//bZGjx6tgoKCb93+RWmpHlj4L00Zm6GMzDZatKpC00qqVDRlclSuDwAAACB5kEgmqfT0dGVnZ0etIrlp0yZJ0iWXXKJLL7203jElC+ZrTvEs3fa6Xz6fT0VTJtNoBwAAAGiBSCSTWNu2baOeSHbq1KnBMYVjx5E4AgAAAKDZTjJr165d1Ka2NiWRBAAAAACJRDKpxbsiCQAAAAASiWRSi2YiuXHjRkkkkgAAAAAaRyKZxGIxtTUvLy8q5wMAAACQumi2k8TatWunl156SZ07d25wTFpamn73u9/p3HPPDXuuTZs2qUOHDmrVipcEAAAAgPDIGpLYtddeq65du4Yd88wzz+gPf/hDkxJJprUCAAAAaAoSySR2zDHH6Jhjjgk7Jjc3V7/61a80e/bssNXGDz/8kEQSAAAAQJOQSKa4c889V3fffbeuvvrqRsdeeOGFcYgIAAAAQLIjkUxxQ4cO1ebNm7Vz585Gx3bp0iUOEQEAAABIdiSSLUBubq5yc3O9DgMAAABAimD7DwAAAABARDxJJM1slpl9YmYfmtnfzayDF3EAAAAAACLnVUXyZUmDnXNDJa2UdJNHcQAAAAAAIuRJIumce8k5Vx389j1Jvb2IAwAAAAAQuURYI3mppPleBwEAAAAAaJqYdW01s1ckda/nplucc/8IjrlFUrWkR8Oc53JJl0vSQQcdFINIAQAAAACRiFki6Zz7XrjbzewiSadJOsk558Kc5yFJD0nSiBEjGhwHAAAAAIgPr7q2jpV0g6TTnXOVXsTQEpQsmK8J48do1JB8TRg/RiULmEEMAAAAoPm8WiM5W1I7SS+b2RIz+71HcaSskgXzVTx9kq4fWaZ3rsvT9SPLVDx9EskkAAAAgGazMLNKE86IESPcokWLvA4jKUwYP0bXjyzTiH45e48tWlWhmQt76MnnXvMwMgAAAADJwMwWO+dG1HdbInRtRQz4/atV0Dd7n2MFfbPl9/u9CQgAAABAyiCRTFE+X76WlO67/HRJaaV8Pp83AQEAAABIGSSSKWpi0WRNK6nSolUVqq5xWrSqQtNKqjSxaLLXoQEAAABIcjHb/gPeKhw7TtJszSyeJf88v3w+n4qmTA4eBwAAAIADR7MdAAAAAMC30GwHAAAAABA1JJIAAAAAgIiQSAIAAAAAIkIiCQAAAACICIkkAAAAACAiJJIAAAAAgIiQSAIAAAAAIkIiCQAAAACIiDnnvI6hycxsg6RSr+NoQGdJG70OAi0KrznEG685xBuvOXiB1x3iLZFfc32dc13quyGpEslEZmaLnHMjvI4DLQevOcQbrznEG685eIHXHeItWV9zTG0FAAAAAESERBIAAAAAEBESyeh5yOsA0OLwmkO88ZpDvPGagxd43SHekvI1xxpJAAAAAEBEqEgCAAAAACJCIhkFZjbWzD41s8/N7Eav40FqM7M+Zva6ma0ws2Vmdo3XMaFlMLN0M/vAzJ73OhakPjPrYGZPmdknwX/vjvE6JqQ2M7s2+Hf1YzN73MyyvI4JqcXM/mxm683s4zrH8szsZTP7LPj/jl7GGAkSyWYys3RJxZLGSTpM0nlmdpi3USHFVUv6hXNukKSjJRXxmkOcXCNphddBoMX4jaQFzrlDJQ0Trz3EkJn1kvQ/kkY45wZLSpf0Q2+jQgr6q6Sx+x27UdKrzrkBkl4Nfp8USCSbb5Skz51zq5xzVZL+T9IZHseEFOacK3POvR/8eocCb656eRsVUp2Z9ZZ0qqQ/eR0LUp+ZtZd0vKQ5kuScq3LObfU0KLQErSS1MbNWkrIlrfU4HqQY59wbkjbvd/gMSQ8Hv35Y0g/iGVNzkEg2Xy9JX9b5fo14U484MTOfpCMk/dvjUJD67pd0vaRaj+NAy9BP0gZJfwlOp/6TmeV4HRRSl3PuK0n3SPpCUpmkbc65l7yNCi1EN+dcmRQoFkjq6nE8TUYi2XxWzzFa4SLmzKytpL9J+plzbrvX8SB1mdlpktY75xZ7HQtajFaShkt60Dl3hKQKJdF0LySf4Lq0MyTlS+opKcfMfuxtVEBiI5FsvjWS+tT5vreYCoEYM7MMBZLIR51zT3sdD1LesZJONzO/AtP3x5jZXG9DQopbI2mNcy402+IpBRJLIFa+J2m1c26Dc26PpKclfcfjmNAyrDOzHpIU/P96j+NpMhLJ5lsoaYCZ5ZtZpgILs5/1OCakMDMzBdYNrXDO3et1PEh9zrmbnHO9nXM+Bf6Ne805xyf1iBnn3NeSvjSzQ4KHTpK03MOQkPq+kHS0mWUH/86eJBo8IT6elXRR8OuLJP3Dw1gi0srrAJKdc67azCZJKlGgw9efnXPLPA4Lqe1YSRdI+sjMlgSP3eyce9G7kAAg6q6W9GjwQ9pVki7xOB6kMOfcv83sKUnvK9Ad/QNJD3kbFVKNmT0u6QRJnc1sjaTbJN0t6Ukzm6jABxrneBdhZMw5lvMBAAAAAJqOqa0AAAAAgIiQSAIAAAAAIkIiCQAAAACICIkkAAAAACAiJJIAAAAAgIiQSAIAAAAAIkIiCQBAE5nZ/zOzJfv9V2tm4+oZ28bM/mVm6RGcf5KZsV8iACDhsY8kAAAHyMwul3S+pBOdc7X73VYkqZVz7jcRnC9b0tvOuSOiGykAANFFRRIAgANgZgMlTZV0wf5JZND5kv4RHHtCsDr5pJmtNLO7zex8M/uPmX1kZv0lyTlXKclvZqPi9kAAADgAJJIAAETIzDIkPSbpOufcF/Xcnimpn3POX+fwMEnXSBoi6QJJA51zoyT9SdLVdcYtknRcjEIHACAqSCQBAIjcdEnLnHP/18DtnSVt3e/YQudcmXNut6T/SnopePwjSb4649ZL6hm9UAEAiL5WXgcAAEAyMbMTJJ0laXiYYTslZe13bHedr2vrfF+rff8eZwXvDwBAwqIiCQBAE5lZR0l/kXShc25HQ+Occ1skpZvZ/slkUwyU9PEBhggAQFyQSAIA0HRXSuoq6cH9tgA5t56xL0kafQDXOFbSK80JEgCAWGP7DwAAYsDMjpD0c+fcBbG8DwAAXqAiCQBADDjnPpD0upmlR3C3zpKmxCgkAACihookAAAAACAiVCQBAAAAABEhkQQAAAAARIREEgAAAAAQERJJAAAAAEBESCQBAAAAABH5/4F4ROeu9SDpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor          # random forest method\n",
    "seed = 73093                                                # set the random forest hyperparameters\n",
    "max_depth = 5\n",
    "num_tree = 1000\n",
    "max_features = 1\n",
    "\n",
    "my_forest = RandomForestRegressor(max_depth=max_depth, random_state=seed,n_estimators=num_tree, max_features=max_features)\n",
    "my_forest.fit(X = depth.values.reshape(-1, 1), y = Npor)  \n",
    "RF_Npor = my_forest.predict(depth_values.reshape(-1,1))\n",
    "plt.subplot(111)\n",
    "plt.plot(depth_values, RF_Npor, label='model', color = 'black')\n",
    "plt.plot(depth, Npor, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.title('Decision Tree Prediction of NPorosity from Depth')\n",
    "plt.xlabel('Z (m)')\n",
    "plt.ylabel('NPorosity')\n",
    "plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Standardized Features\n",
    "\n",
    "Let's work with standardized features. I switched to Gaussian transform (see below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# scaler = StandardScaler()                                  # instantiate the scaler \n",
    "# sfeatures = scaler.fit_transform(df.values)                # standardize all the values extracted from the DataFrame \n",
    "# df_st = pd.DataFrame()                                     # instantiate a new DataFrame\n",
    "# df_st = pd.DataFrame(sfeatures, index=df.index, columns=df.columns) # copy the standardized values into the new DataFrame\n",
    "# depth_st = df_st['Depth']\n",
    "# Npor_st = df_st['Nporosity']\n",
    "# df_st.head()                                               # preview the the new DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Anamorphosis\n",
    "\n",
    "Let's transform the features to standard normal. \n",
    "\n",
    "* Gaussian distribution\n",
    "* mean of 0.0\n",
    "* standard deviation of 1.0\n",
    "\n",
    "The porosity feature was 'transformed' to Gaussian previously, but there is an opportunity to clean it up.\n",
    "\n",
    "* compare the original and transformed below\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Nporosity</th>\n",
       "      <th>Depth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.356312</td>\n",
       "      <td>-2.026808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.219599</td>\n",
       "      <td>-1.780464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.780464</td>\n",
       "      <td>-1.534121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.091620</td>\n",
       "      <td>-1.356312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.221119</td>\n",
       "      <td>-1.213340</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Nporosity     Depth\n",
       "0  -1.356312 -2.026808\n",
       "1  -2.219599 -1.780464\n",
       "2  -1.780464 -1.534121\n",
       "3  -1.091620 -1.356312\n",
       "4  -0.221119 -1.213340"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import QuantileTransformer\n",
    "import geostatspy.geostats as geostats\n",
    "\n",
    "# I would like to use the scikit-learn method, but it creates outliers!\n",
    "#nscore = QuantileTransformer(n_quantiles=40, random_state=73, output_distribution = 'normal') \n",
    "#nsfeatures = nscore.fit_transform(df)                                                             # standardize all the values extracted from the DataFrame \n",
    "#df_ns = pd.DataFrame()                                      # instantiate a new DataFrame\n",
    "#df_ns = pd.DataFrame(nsfeatures, index=df.index, columns=df.columns) # copy the standardized values into the new DataFrame\n",
    "\n",
    "#Transform to Gaussian with GeostatsPy\n",
    "df_ns = pd.DataFrame()   \n",
    "df_ns['Nporosity'], tvPor, tnsPor = geostats.nscore(df, 'Nporosity') # nscore transform for all facies porosity \n",
    "df_ns['Depth'], tvdepth, tnsdepth = geostats.nscore(df, 'Depth')  # nscore transform for all facies permeability\n",
    "depth_ns = df_ns['Depth']\n",
    "Npor_ns = df_ns['Nporosity']\n",
    "df_ns.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make some good cumulative distribution function plots to check the original and transformed variables.\n",
    "\n",
    "* the results look very good\n",
    "\n",
    "We are doing this because we will need a Gaussian distribution for the predictor feature for orthogonality.  More later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)                                              # plot original sand and shale porosity histograms\n",
    "plt.hist(df['Depth'], facecolor='red',bins=np.linspace(0.0,10.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.0,10.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Depth (m)'); plt.ylabel('Frequency'); plt.title('Original Depth')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)  \n",
    "plt.hist(df_ns['Depth'], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Depth (Standard Normal)'); plt.ylabel('Frequency'); plt.title('Nscore Depth')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['Nporosity'], facecolor='red',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (Near Gaussian)'); plt.ylabel('Frequency'); plt.title('Original Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df_ns['Nporosity'], facecolor='blue',bins=np.linspace(-3.0,3.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (Standard Normal)'); plt.ylabel('Frequency'); plt.title('Nscore Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Linear Regression Model with Standardized Features\n",
    "\n",
    "Let's repeat the linear regression model, now with the standardized features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_ns = LinearRegression() \n",
    "lin_ns.fit(depth_ns.values.reshape(-1, 1), Npor_ns) \n",
    "slope_ns = lin_ns.coef_[0]\n",
    "intercept_ns = lin_ns.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now visualize the model. \n",
    "\n",
    "* Quite a poor fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "depth_ns_values = np.linspace(-3.0,3.0,100)\n",
    "plt.subplot(111)\n",
    "plt.plot(depth_ns, Npor_ns, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.plot(depth_ns_values, intercept_ns + slope_ns*depth_ns_values, label='model', color = 'black')\n",
    "plt.title('Standardized Porosity vs Standardized Depth')\n",
    "plt.xlabel('Standardized Depth')\n",
    "plt.ylabel('Standardized Porosity')\n",
    "plt.legend(); plt.xlim(-2,2)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial Regression \n",
    "\n",
    "We will do polynomial regression by hand:\n",
    "\n",
    "* create the polynomial basis expansion of the original predictor feature\n",
    "\n",
    "* perform linear regression on the polynomial basis expansion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Values</th>\n",
       "      <th>0th</th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "      <th>3rd</th>\n",
       "      <th>4th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.026808</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.026808</td>\n",
       "      <td>4.107951</td>\n",
       "      <td>-8.326029</td>\n",
       "      <td>16.875264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.780464</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.780464</td>\n",
       "      <td>3.170053</td>\n",
       "      <td>-5.644167</td>\n",
       "      <td>10.049238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.534121</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.534121</td>\n",
       "      <td>2.353526</td>\n",
       "      <td>-3.610592</td>\n",
       "      <td>5.539084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.356312</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.356312</td>\n",
       "      <td>1.839582</td>\n",
       "      <td>-2.495046</td>\n",
       "      <td>3.384060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.213340</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.213340</td>\n",
       "      <td>1.472193</td>\n",
       "      <td>-1.786270</td>\n",
       "      <td>2.167352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Values  0th       1st       2nd       3rd        4th\n",
       "0 -2.026808  1.0 -2.026808  4.107951 -8.326029  16.875264\n",
       "1 -1.780464  1.0 -1.780464  3.170053 -5.644167  10.049238\n",
       "2 -1.534121  1.0 -1.534121  2.353526 -3.610592   5.539084\n",
       "3 -1.356312  1.0 -1.356312  1.839582 -2.495046   3.384060\n",
       "4 -1.213340  1.0 -1.213340  1.472193 -1.786270   2.167352"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly4 = PolynomialFeatures(degree = 4)                       # instantiate polynomial expansion \n",
    "depth_ns_poly4 = poly4.fit_transform(depth_ns.values.reshape(-1, 1))# apply polynomial expansion to transformed predictor feature\n",
    "df_depth_ns_poly4 = pd.DataFrame({'Values':depth_ns.values,'0th': depth_ns_poly4[:,0],'1st': depth_ns_poly4[:,1],'2nd': depth_ns_poly4[:,2], '3rd': depth_ns_poly4[:,3], '4th': depth_ns_poly4[:,4]}) # make a new DataFrame from the vectors\n",
    "df_depth_ns_poly4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's check the correlation between the polynomial basis expansion of the original predictor features data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "      <th>3rd</th>\n",
       "      <th>4th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1st</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059005</td>\n",
       "      <td>0.850982</td>\n",
       "      <td>0.111841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>0.059005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.147450</td>\n",
       "      <td>0.938188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd</th>\n",
       "      <td>0.850982</td>\n",
       "      <td>0.147450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.243912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4th</th>\n",
       "      <td>0.111841</td>\n",
       "      <td>0.938188</td>\n",
       "      <td>0.243912</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1st       2nd       3rd       4th\n",
       "1st  1.000000  0.059005  0.850982  0.111841\n",
       "2nd  0.059005  1.000000  0.147450  0.938188\n",
       "3rd  0.850982  0.147450  1.000000  0.243912\n",
       "4th  0.111841  0.938188  0.243912  1.000000"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_depth_ns_poly4.iloc[:,2:].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the Polynomial Expansion Features' Pairwise Relationship"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x27fbc2a4490>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_depth_ns_poly4.iloc[:,2:],vars=['1st','2nd','3rd','4th'],markers='o', kind='reg',diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the polynomial expansion over the standardized depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C5v8g7EpEREREREQ6nqrtfgTavsfBwE/v0yk6Z9jMGQoHXgMr7oMt74RdjYiIiIiISMfywS984Jz8ezDbp1N0zrAJMPYHkNEX5n3Lj6AkIiIiIiIie1b0EXx0C+x/OfSYuM+n6bxhMzUPJvzSDxi06l9hVyMiIiIiItIxzPsuRDNgwi+adZrOGzYBhn8Buk+E+ddCvCLsakRERERERNq3DS/C2sdh7A8hs1+zTtW5w2Yk6vsYl66AxTeFXY2IiIiIiEj7lYjD3Gsgez8/Bk4zde6wCdDvOBh0Jiz4pW6FIiIiIiIisivL7oLC9/3dPaIZzT5d5w+bAJP/4G9EOvfbYVciIiIiIiLS/lRsgvk/hL7HwpDzWuSUXSNs5gyDMT+EVQ/BhufDrkZERERERKR9mf99iJVAwW37fKuThrpG2AQY813I2R9mfx3ilWFXIyIiIiIi0j5sfgM++Ssc+C3oNrrFTtt1wmY0AwpuhaIlsPgPYVcjIiIiIiISvkQM3v0aZA2Ccf/XoqfuOmETYMApMOgz8OEvoHRV2NWIiIiIiIiE6+M7oPA9P85Nak6LnrprhU2AKX8AnB/SV0REREREpKsq3wjv/wj6nQiDz2nx03e9sJm9n28eXv0orHsm7GpERERERETCMe+7EC/3lxu20KBAybpe2AR/4WvuSJj9DYhXhF2NiIiIiIhI29r0Kqy4F0Z/F/JGtspTdM2wGU336b1kKSyaEXY1IiIiIiIibSdRDe9eBVlDYOx1rfY0XTNsAvQ/EYZ8FhZcDyXLw65GRERERESkbXx0K+z4EKbcDClZrfY0XTdsAkz+PVgU5kwPuxIREREREZHWV7YO3v8J9D8VBp3Zqk/VtcNm1iAY9xNY+zisfSLsakRERERERFrXvO9AogoKbmmVQYGSde2wCTDqm5A3GmZfDbHysKsRERERERFpHRtfgpX/gDHXQu7+rf50CpvRNJh6G5QuhwW/CrsaERERERGRlhev9IMCZQ+DMd9vk6dU2AToeywM/TwsvBEKPwi7GhERERERkZa14AYoWuQb2lIy2+QpFTZrTP49pHWHt78MiXjY1YiIiIiIiLSMwgWw8Fcw9CIYcGqbPa3CZo2MfD/079a3/VDAIiIiIiIiHV0iDm9fDql5MPkPbfrUCpvJ9rsQBpwG7/0QSlaEXY2IiIiIiEjzfHybb1CbfDNk9G7Tp1bYTGYGU+8Ai8C7V4JzYVckIiIiIiKyb0pX+oa0/qfC0M+1+dMrbDaUPQQm3gDr/wcr7gu7GhERERERkb3nHLxzpV8++I5Wv6dmYxQ2GzPiq5A/DeZMh4pNYVcjIiIiIiKyd1Y8AOufgYm/guz9QilBYbMxkSgccjfEimHONWFXIyIiIiIi0nQVm2HuN6HXoTDiqtDKUNjclW5jYOx1sPIBWPtk2NWIiIiIiIg0zdxroLrIN6BFoqGVobC5O2N+AN3GwrtfherisKsRERERERHZvXVPw4r7YcwPofvYUEtR2NydaBoc/GcoW+NHcRIREREREWmvqov9oEB5o2HsD8KuRmFzj3pPg5HfgI9ug81vhF2NiIiIiIhI4977EZSthkPugWh62NUobDbJxOshazC8/SWIV4ZdjYiIiIiISH1b3oKPboGRV/kGs3ZAYbMpUnPg4DuhaBF88NOwqxEREREREakTK4e3LoOsQf5WJ+2EwmZTDTgF9r8cFv1G3WlFRERERKT9eO8HULQEDv0rpOaGXU0thc29Mfn3vjvtm5dCrDTsakREREREpKvb+BIsudmPM9Pv+LCrqUdhc2+k5sGhf4OSZTDve2FXIyIiIiIiXVnVDnjzMsgdCZNuDLuanYQaNs2su5k9bGaLzWyRmU0zs55m9pyZfRzMe4RZ4076Hg2jpsPHt8P6Z8OuRkREREREuqq510D5Gpj2d0jJCruanYTdsnkz8Ixz7kBgIrAI+D7wgnNuBPBCsN6+TLze37vmrS9C1fawqxERERERka5mzePwyV9hzA8g/5Cwq2lUaGHTzPKAo4B7AJxzVc65QuBM4G/BYX8Dzgqjvt1KyfTfHlRsgNlXh12NiIiIiIh0JRWb4Z0vQ49JMO7HYVezS2G2bA4HNgN/NbN5Zna3mWUDfZ1z6wGCeZ8Qa9y1XgUw9kew4j5Y9UjY1YiIiIiISFfgHLx7JVQVwrR7IZoWdkW7FGbYTAEmA3c45w4CStmLLrNmdoWZzTaz2Zs3b26tGndv3HXQcwq8+xUo3xBODSIiIiIi0nWsuB9WPwoTfgHdx4VdzW6FGTbXAGucc28H6w/jw+dGM+sPEMw3NfZg59xdzrkC51xB796926TgnURSfXfa6hJ45wr/LYOIiIiIiEhrKF0Ns78OvQ+HA78ddjV7FFrYdM5tAFab2ahg0/HAQuBx4NJg26XAYyGU13TdxsCkG2Dtf+GTmWFXIyIiIiIinZFLwNtfBBfzt2OMRMOuaI9SQn7+bwD3m1ka8AnwBXwAfsjMLgdWAZ8Nsb6mGfVNWPMYzPkm9D0WcoaGXZGIiIiIiHQmH98BG56HqX+C3P3DrqZJQg2bzrn5QEEju45v41KaxyJw6F/hqQnw1hfguOc7xDcNIiIiIiLSARR9BPO+C/1PgQOuCLuaJgv7PpudR84wmHIzbHoZFv067GpERERERKQziFfCrAsgmgmH3A1mYVfUZAqbLWn4F2DI+fD+j2HzG2FXIyIiIiIiHd3878P2eb4nZdbAsKvZKwqbLckMDr4TsobAG5+Dqu1hVyQiIiIiIh3V2idgyU0w8moYdEbY1ew1hc2WltYNDn8QytbC21/W7VBERERERGTvla2Fty6DHpPgoN+EXc0+UdhsDfkHw8TrYfUjsPSusKsREREREZGOJBGHNy6GeIVvyIqmh13RPlHYbC2jvwP9ToK506Hww7CrERERERGRjmLBr/zAowW3Qd6osKvZZwqbrcUiMO3vkNoNZp0PsbKwKxIRERERkfZu02vw4U9h6EUw7JKwq2kWhc3WlNkXpt0LOxbC3GvCrkZERERERNqzym3wxkWQPRym3tGhbnPSGIXN1tb/RBhzrb92c9W/wq5GRERERETaI+fg7cuhYgMc8SCk5oZdUbMpbLaFCb+AXof40WlLVoRdjYiIiIiItDcf3wFr/gMTb4SeU8KupkUobLaFSCoc/g/AwawLIVEddkUiIiIiItJebH8P5n4LBpwGB04Pu5oWo7DZVnKGwcF/hq1vwfv/F3Y1IiIiIiLSHlSXwKwLIL0nHDrTDzTaSXSen6Qj2O88OOArsPDXsOaxsKsREREREZEw1VynWfwRHHY/ZPQOu6IWpbDZ1qbcDD2nwpuXQNFHYVcjIiIiIiJhWXITrHoIJt4AfY8Nu5oWp7DZ1qLpcOTDEEmD1872zeYiIiIiItK1bHwF5n0XBp8No78bdjWtQmEzDNlD4PAHoWgRvP0l33wuIiIiIiJdQ9lamHUe5B4Ah/61w99Pc1cUNsPS73iYcD2s+icsuTnsakREREREpC3Eq+D1z0KsFI58FFLzwq6o1ShshmnMtTDoLJj3Hdj0WtjViIiIiIhIa5v3bdjypm/R7DYm7GpalcJmmMz88MY5+8Pr50H5+rArEhERERGR1rL8PvjoVjjw2zDks2FX0+oUNsOW1s03n8eKfXN6vCrsikREREREpKVtfw/euQL6HA2Tbgy7mjahsNkedB8Lh9wDm2f5EalERERERKTzqNru70SR1gMO/ydEUsKuqE10jZ+yI9jvfNjyNiz5A+QfAkM/F3ZFIiIiIiLSXC4Bb1wCZavh+Fcgs2/YFbUZtWy2Jwf9Gvoc5W+Hsv39sKsREREREZHm+vCXsO4JmPwH6D0t7GralMJmexJJ9c3qaT3glU9D+cawKxIRERERkX216mH44Ccw7BIY8bWwq2lzCpvtTWY/OPpxqNwMr30G4hVhVyQiIiIiIntr62x48xLInwYH3+nvRNHFKGy2Rz2nwLR7/f133v4SOBd2RSIiIiIi0lRla+HVMyG9Nxz5b4hmhF1RKBQ226sh58CEX8KK+2HBr8KuRkREREREmiJW5oNmdREc80SXGhCoIY1G256N/SEULYb3fwR5o2DIuWFXJCIiIiIiu+ISvuvstrn+0rju48OuKFRq2WzPzOCQP0P+Yf5Nu3V22BWJiIiIiMiuvP9jWP0IHDQDBp4edjWhU9hs76IZcNS/IaOPb44vWxt2RSIiIiIi0tDy+2HB9bD/5XDgNWFX0y4obHYEGX3g6P/6ft+vnun7gYuIiIiISPuw+U14+3LoczQU3N4lR55tjMJmR9F9PBz+oO///eYlvj+4iIiIiIiEq3QlvHYWZA2GIx+BaFrYFbUbCpsdycBP+f7fqx/x/cFFRERERCQ81cXw8ukQr/Q9EdN7hV1Ru6LRaDuaA6+BokW+P3jOMN8nXERERERE2laiGl7/rP9sfszT0O3AsCtqdxQ2OxozmHo7lK2Gd74CGX010pWIiIiISFtyCXjrclj/Pzj4z9D/xLArapfUjbYjiqTCEQ9Dj4Pg9fP8BckiIiIiItI25v8AVtwLE34BB3wp7GrarU7bslldXc2aNWuoqKgIu5QWkZGRwaBBg0hNTfUbUnPgmCfh2cPhldPhxNeh2+hwixQRERER6ewW3wSLfgMjvgZjrwu7mnat04bNNWvWkJuby9ChQ7EOPvSwc46tW7eyZs0ahg0bVrcjow8c9z949jB46RQ46Q3IGhheoSIiIiIindmKB2HuNTD4bJjyR93iZA86bTfaiooKevXq1eGDJoCZ0atXr8ZbaXOG+wuSq7b7wFlV2Ob1iYiIiIh0ehueh7cugT5HwWH3QyQadkXtXqcNm0CnCJo1dvuz9DwIjnoUipfAq2dCvHN0HRYRERERaRe2zYNXPwO5o+CoxyCaEXZFHULoYdPMomY2z8yeCNZ7mtlzZvZxMO8Rdo3N8cwzzzBq1CgOOOAAbrzxRgBmzpzJunXrao8ZOnQoW7Zsad4T9TsBDv07bHoV3rgIEvHmnU9ERERERKDkE3j5VEjrCcc+A2ndw66owwg9bALfBBYlrX8feME5NwJ4IVjvkOLxOFdddRVPP/00Cxcu5B//+AcLFy7cKWy2mKEXwOQ/wOpHYc7V4FzLP4eIiIiISFdRsQlePNnfU/PYZzQ+yl4KNWya2SDgU8DdSZvPBP4WLP8NOKuNy2ox77zzDgcccADDhw8nLS2NCy64gEceeYTZs2dz0UUXMWnSJMrLywG45ZZbmDx5MuPHj2fx4sX7/qQHTofR34WPb4cPf9kyP4iIiIiISFdTXQwvfwrK18LRT+jOD/sg7NFobwK+B+QmbevrnFsP4Jxbb2Z9GnugmV0BXAEwZMiQ3T7J9OnTmT9/fguUW2fSpEncdNNNuz1m7dq1DB48uHZ90KBBvP322xQUFDBjxgwKCgpq9+Xn5zN37lxuv/12ZsyYwd13393YKZtY3I1QvgE++DGk5voAKiIiIiIiTRMr87cX3D4Pjvw39J4WdkUdUmgtm2Z2OrDJOTdnXx7vnLvLOVfgnCvo3bt3C1fXMlwj3Vh3NdDP2WefDcCUKVNYsWJF857YInDoX2DwOX5o5o/vaN75RERERES6iniFH3Rz8+sw7T4Y9OmwK+qwwmzZPBw4w8xOAzKAPDO7D9hoZv2DVs3+wKbmPtGeWiBby6BBg1i9enXt+po1axgwYAAffPDBTsemp6cDEI1GicVizX/ySAoc9gC8dg68+zWIpMP+X2z+eUVEREREOqt4lf/8vOF5OHSmHxNF9lloLZvOuR845wY554YCFwAvOucuBh4HLg0OuxR4LKQSm23q1Kl8/PHHLF++nKqqKh588EHOOOMMcnNzKS4ubv0Comlw5L+g30nw9pdgxQOt/5wiIiIiIh1RohpmXQDrnoKD74Thl+75MbJb7WE02oZuBE40s4+BE4P1DiklJYVbb72Vk08+mdGjR3PeeecxduxYLrvsMq688sp6AwS1mmgGHPVv6HM0vHkJrHqkdZ9PRERERKSjScT9Z+U1/4YpN8MBV4RdUadgjV1X2NEUFBS42bNn19u2aNEiRo/uXCNGNetnqi6Bl06Gre/AkY+q77mIiIiICIBLwFtfhOV/g0m/gTHfDbuids/M5jjnCvZ0XHts2ZTWkJoDxzwFPSbB6+fC+mfDrkhEREREJFzOwbtf9UFz/M8UNFuYwmZXktYNjv0f5I32I2xtfDnsikREREREwuEczJkOS++CMT+Acf8XdkWdjsJmV5PeE457DnKG+3sHbZ4VdkUiIiIiIm3LOZj/ffjojzDqGph4PeziFoWy7xQ2u6KM3nDcC5A50F/HufGlsCsSEREREWkbzsHcb8Gi38CIr8Lk3ylothKFza4qsx+c8ApkD4WXT4N1z4RdkYiIiIhI63IJf43mkptg5NVQcJuCZitS2OzKMvvB8S8H13CeAav/HXZFIiIiIiKtIxGDNy+DpXf6azSn3KSg2coUNlvRF7/4Rfr06cO4ceNqt82cOZN169bVrg8dOpQtW7aEUZ6XkQ/Hvwg9psDrn4UVD4RXi4iIiIhIa4hXwawLYcW9MOGXMOlXCpptQGGzFV122WU880z97qkNw2a7kNYdjnsWeh8Jb1wMy/4SdkUiIiIiIi0jXgGvnQOrH4bJv4dx14VdUZehsNmKjjrqKHr27Fm7/vDDDzN79mwuuugiJk2aRHl5OQC33HILkydPZvz48SxevDicYlNz4Zgnof9J8PblsOTWcOoQEREREWkpsVJ45dOw7gmYegcceE3YFXUpKWEX0BamT4f581v2nJMmwU037d1jzj33XG699VZmzJhBQUFB7fb8/Hzmzp3L7bffzowZM7j77rtbtNYmS8mCox6DWRfAnG9AvAzGfC+cWkREREREmqO6CF7+FGx5Aw6dCcMvDbuiLkctm+3A2WefDcCUKVNYsWJFuMVE0+GIh2C/C2D+tfD+T/3w0CIiIiIiHUXlNnjhBNjyFhz2DwXNkHSJls29bYFsa+np6QBEo1FisVjI1QCRVJh2H0Sz4MOfQdU2mPwHiETDrkxEREREZPfK1sBLp0LxR3DkozDo02FX1GV1ibDZnuTm5lJcXBx2GXsWicIhf/aDBy3+PZSvg8Pug2hG2JWJiIiIiDSu8EN4+VSo2gHHPAX9jg+7oi5N3Whb0YUXXsi0adNYsmQJgwYN4p577uGyyy7jyiuvrDdAULtlEZj8Oz9q1+pH4MWTfJcEEREREZH2ZuMr8NwR4OJw4msKmu2AuU5wPV5BQYGbPXt2vW2LFi1i9OjRIVXUOkL9mVb+E968BHL2h2Ofgewh4dQhIiIiItLQyofgzc9DzvDgs+p+YVfUqZnZHOdcwZ6OU8umNM1+58Ox//PdaZ+dBtvfD7siERERERFYfJO/m0KvqXDiLAXNdkRhU5qu7zFw4uuAwfNHwoYXw65IRERERLoql4C534G518Dgz8Cxz0F6zz0/TtqMwqbsne7j4KQ3IWswvHwKrPhH2BWJiIiISFcTr4Q3LoLFv4ORX4fDH4KUzLCrkgb2KmyaWQ8zG2tmw81MQbWryh7sWzjzD4M3PgcLf6t7cYqIiIhI26gqhJdOgZUPwqRfw5Q/6hZ97dQeb31iZt2Aq4ALgTRgM5AB9DWzt4DbnXMvtWqV0v6kdffXcL55Ccz/HhQvgYLbIJoedmUiIiIi0lkVfQSvngHFy2DavTDs4rArkt1oyn02Hwb+DhzpnCtM3mFmU4DPm9lw59w9rVCftGfRdDj8H5A7Ehb8EooW+xvnZvQJuzIRERER6WzWPwuvnw+RFDj+BehzVNgVyR7ssSusc+5E59y9DYNmsG+Oc266gmbjVq9ezbHHHsvo0aMZO3YsN998MwAzZ85k3bp1tccNHTqULVu2hFVm81gEJv4CDn8Qts2FZ6bC9vlhVyUiIiIinYVzsPhmePlUfznXye8qaHYQTb7u0swub7AeNbOftHxJnUdKSgq/+93vWLRoEW+99Ra33XYbCxcu3Clsdgr7ne9vnksCnj0cVj0SdkUiIiIi0tHFK+GdL8Pc6TDwDDjxDcgZGnZV0kR7M8jP8Wb2lJn1N7NxwFtAbivV1Sn079+fyZMnA5Cbm8vo0aNZsGABs2fP5qKLLmLSpEmUl5cDcMsttzB58mTGjx/P4sWLwyx73/Wc4r9p6j4BXj8XPviZH5JaRERERGRvVWyCF4+HZffA2B/BkY9Aak7YVcleaMo1mwA45z5nZucDHwBlwIXOuVmtVlkLmv7MdOZvmN+i55zUbxI3nXJTk49fsWIF8+bN495776WgoIAZM2ZQUFBQuz8/P5+5c+dy++23M2PGDO6+++4WrbfNZPaDE16Cd74CH/wUCj+EaTMhJTvsykRERESko9g+H145Eyo3+8u19js/7IpkH+xNN9oRwDeBR4AV+IGBslqprk6lpKSEc845h5tuuom8vLxGjzn77LMBmDJlCitWrGjD6lpBNAMOnQkHzYA1j8JzR0DpqrCrEhEREZGOYPWj/rIsEv4yLQXN9uOdd+CCC5p8eJNbNoH/Alc5514wMwO+BbwLjN27Ctve3rRAtrTq6mrOOeccLrrootpA2Zj0dH/LkGg0SiwWa6vyWo8ZjP42dBsDsy6AZ6bAYfdD/5PCrkxERERE2qNENbz3I1j0G+h1KBz1b99rTsIVj8Njj8Hvfw+zZsEuGs8aszfXbB7snHsBwHm/A87au0q7Fuccl19+OaNHj+Zb3/pW7fbc3FyKi4tDrKwNDTgVTn4HMvr5m+++/2NIxMOuSkRERETak7K18MJxPmgecKW/LEtBM1zFxfDHP8KIEXDOObBuHdx8M6xZ0+RT7DFsmtkRAM65oob7nHMfm1leMGCQNDBr1izuvfdeXnzxRSZNmsSkSZN46qmnuOyyy7jyyivrDRDUqeWNgpPfhuGXwoe/gJdOgvKNYVclIiIiIu3B+ufg6YNg+zzfE+7gO/xlWRKO1avhe9+DwYPhm9+E/v3h4Yfh44/h6qsht+ljxJpzbvcHmP0BOAR4BpgDbAYygAOAY4H9gG87597dxx+n2QoKCtzs2bPrbVu0aBGjR48OqaLW0Sl+pmV/hdlXQVp3f7G37pEkIiIi0jUl4r4h4sOf+0uvjngYuh0YdlVdk3O+i+wf/wiPPurXzz0XrrkGDj10p8PNbI5zrqCRM9Wzx2s2nXPXmFkP4Fzgs0A/oBxYBPypo4xIK+3E/l+AXgXw2rnwwrEw4XoY8z2wvenRLSIiIiIdWsUmeOMi2PA8DLsEpt6uuxeEobwcHnzQh8z586F7d5g+Hb7+dRg6tNmnb9IAQc657Wb2d/wtT4YmPe54QGFT9k738XDKbHj7y/DeD2DzazDt75DeK+zKRERERKS1bXrNDyBZtQ0OuQeGf8EPLiltZ/VquP12+POfYetWGDcO7rwTLroIslsu9O/NaLSPAYXAXKCixSqQrik1Fw7/h+9GO/ca30//8Aeh92FhVyYiIiIirSERh8Uz4L3rIGc4HPMU9JgYdlVdh3Pw6qtw663w73/79TPPhG98A445plUC/96EzUHOuVNavALpusxg5Neg18Hw+nnw/JEw5ocw/scQSQ27OhERERFpKaUr4c1LYdMrMOSzcMjdkNr0W2hIM+zYAX//O/zpT7BwIfTsCd/+Nnzta7Dffq361HsTNt8ws/HOuQ9arRrpmnoVwGnzYc43YcEvYf0zcNh9fhRbEREREem4nIMV9/sBIl0CDv0rDLtU3Wbbwty5cMcd8MADUFYGU6fCX/4C558PWVltUsIew6aZfQC44NgvmNknQCVg+FtuTmjdEqVLSM3z//kMOB3eucJ3qz1oBoz4qv4zEhEREemIKrfBu1+FVQ9B78P9GB05w8OuqnMrK4N//tOHzHffhcxM+Nzn4KtfhSlT2rycprRsnt7qVXRSFRUVHHXUUVRWVhKLxTj33HP52c9+xsyZMznppJMYMGAAAEOHDmX27Nnk5+eHXHE7MOQcyJ8Gb3/RfwO29gk49C+6qa+IiIhIR7LheXjzMqjYCBOvh9HXQiQadlWd14cfwt13w9/+BoWFMHo03HwzXHKJH2E2JE259cnKtiikM0pPT+fFF18kJyeH6upqjjjiCE499VRmzpzJuHHjasOmNJA1AI55Gj66DeZ/F54aBwf/GQZ/JuzKRERERGR3YuX+bgNLboa8A+Hox6Bn27eodQklJb4V8+674a23IDUVPvMZ34p59NHtonegbm7YisyMnJwcAKqrq6muruaVV15h9uzZXHTRRUyaNIny8nIAbrnlFiZPnsz48eNZvHhxmGW3D2Yw6utwylzI2g9eOxve+iJU7Qi7MhERERFpzLZ58L+pPmiO/DqcMkdBs6U557vHXnEF9O8PX/qSHwDod7+Ddet8+GylkWX3xd4MENSizGww8HegH5AA7nLO3WxmPYF/4u/nuQI4zzm3vVlPNn26v0lpS5o0CW66aY+HxeNxpkyZwtKlS7nqqqu49tprefrpp5kxYwYFBQW1x+Xn5zN37lxuv/12ZsyYwd13392y9XZU3UbDSW/Chz+HhTfA+mf9TX8HnRF2ZSIiIiICvjXzw5/BohmQ0QeOeQYGnBx2VZ3Ltm1+oJ8//xnef99fi3n++T5sHnZYuwmXDYXZshkDvu2cGw0cClxlZmOA7wMvOOdGAC8E6x1WNBpl/vz5rFmzhnfeeYcPP/yw0ePOPvtsAKZMmcKKFSvasMIOIJoGE38JJ74J6T3h1TPh9fOhfGPYlYmIiIh0bRtfhqcmwMJfw/DL4FMLFDRbSiwGTz0F553nWzG/8Q3fVfaOO2D9evjrX+Hww9tt0IQQWzadc+uB9cFysZktAgYCZwLHBIf9DXgZuLZZT9aEFsjW1r17d4455hieeeaZRvenp6cDPpzGYrG2LK3jyD8YTp4Ni37rWzo3PAeTf6/hs0VERETaWlUhzPseLPuzH2H2uBeg33FhV9U5LFzoB/q5914fKvPz/XWYl14KBx0UdnV7pV1cs2lmQ4GDgLeBvkEQrQmkfUIsrVk2b95MYWEhAOXl5Tz//PMceOCB5ObmUlxcHG5xHVU0DcZdB6e+B93GwltfgJdOgpJPwq5MREREpGtY/W94cgx8cg+M/i6c9oGCZnNt3+5bLA85BMaO9ddgTp0Kjz4Ka9f6xrMOFjQhxJbNGmaWAzwCTHfOFVkTW6jM7ArgCoAhQ4a0XoHNsH79ei699FLi8TiJRILzzjuP008/ncrKSq688koyMzN58803wy6zY+p2IJzwCiy9E+ZdC0+Ogwm/gFHfhEjob2sRERGRzqd8Pcz+Oqx+FLpPhKP/qwGAmqOyEp5+Gu67D/77X6iqgvHj4fe/9/fG7Ns37AqbzZxz4T25WSrwBPA/59zvg21LgGOcc+vNrD/wsnNu1O7OU1BQ4GbPnl1v26JFixg9enQrVR6OzvgztYjS1fDu12DdE/4/vILbIP+QsKsSERER6RwScf8F/3vXQbwcxv8URn8bIqlhV9bxJBIwaxbcfz889JBv0ezTBy64oK6bbAe4PMzM5jjnCvZ0XJij0RpwD7CoJmgGHgcuBW4M5o+FUJ50JNmD4ejHYdVDMPcaePZQGP4FmHgDZHb8b4REREREQrPpNZj9DSh8D/oeB1PvgLyRYVfV8Sxa5Fsw778fVq6ErCw4+2y4+GI4/nhI6Zw988L8qQ4HPg98YGbzg20/xIfMh8zscmAV8NlwypMOxQz2Ox8GnAYf/gKW3ASrH4HxP4ORV+mbNxEREZG9UbYW5n0XVv4DsobAEf+Cwed0iFa3dmPlSn/fywcfhHnzIBKBk06C66+HM8+EnJywK2x1YY5G+zqwq3fr8W1Zi3Qiqblw0G9g/8thznTf0rnszzDlFl24LiIiIrIn8UpY/AdY8EtIxGDc/8GY70NKVtiVdQzr18O//uUDZs3YLIccAn/4g+8q269fuPW1sc7ZXiuSNwqOeQrW/tcHzhePh8HnwuTfQXb7HFBKREREJFRrn4K506H4Yxh0lv/clDM87Kravy1b4JFHfMB85RVwDiZOhBtugPPPh2HDwq4wNAqb0nmZwaAzoP9JsOh3sOB6WPekH6J79Hd8K6iIiIhIV7djoR/df90T/gv7Y//nPz/Jrm3aBP/5jw+ZL7wA8TiMGgU/+YkPmAceGHaF7YLCpnR+0Qx/b85hl/hrDz78OXx8B4z7MRxwhb93p4iIiEhXU7YG3v8JLJ8JKTlw0G9h5NX6bLQr69fDv/8NDz/sWzATCTjgAPjud30X2QkTdE1rA5GwC+gK4vE4Bx10EKeffjoAM2fOZN26dbX7hw4dypYtW8Iqr+vIHgxHPAgnvQ3dxsKcb8CTo2HFg+ASYVcnIiIi0jaqtvuWzP+OgBX3wajpcMYnvueXgmZ9q1fDzTfDkUfCwIFw1VU+dP7whzB/Pnz0ke8uO3GigmYj1LLZBm6++WZGjx5NUVER4MPmuHHjGDBgQMiVdVH5B8PxL8L6/8H8a+GNC2HRb+GgX0O/E8KuTkRERKR1xMrho1thwa+gegcM+zxM+Dlk7xd2Ze2Hc7Bwoe8i+5//wOzZfvv48fDTn8K558KYMSEW2LGoZbOVrVmzhieffJIvfelLADz88MPMnj2biy66iEmTJlFeXg7ALbfcwuTJkxk/fjyLFy8Os+SuwQwGnAKnzoNp90LVVnjxRHjxJNg2N+zqRERERFpOIg7L/gJPjIT534Peh8Gp82Ha3xQ0wV9vOWuW7w47ciSMGwc/+hFEo/CrX8GSJfD++/DjHyto7qWu0bI5Zzpsn9+y5+wxCabctMfDpk+fzm9+8xuKi4sBOPfcc7n11luZMWMGBQUFtcfl5+czd+5cbr/9dmbMmMHdd9/dsvVK4ywCwy6GIZ/113Eu+CU8MwUGn+2H+u4xKewKRURERPZNIgYr/+k/3xQthl4H+y/Z+x4TdmXhKyuDF1+Exx6Dxx/3A/6kpsLxx8N3vgOf/jSoF2KzdY2wGZInnniCPn36MGXKFF5++eXdHnv22WcDMGXKFB599NE2qE7qiabDgdNh+Bdg8e9hyc2w+lEYeIYPnb0K9ngKERERkXYhUQ0r7ocPr4eSpdB9PBzxsP8yvStfV7h6NTz5JDzxhB9BtqIC8vLgtNPgrLPg1FP9urSYrhE2m9AC2RpmzZrF448/zlNPPUVFRQVFRUVcfPHFjR6bnp4OQDQaJRaLtWWZkiytG0z4GRx4DSy5BZb8Af43FQac5kNn/qFhVygiIiLSuHgVLP+7vyazdDn0OAiO/Le/FZx1wavn4nF4910fLp94At57z28fPhy+8hX41Kfg6KMhTYMitZYu+K5rOzfccANr1qxhxYoVPPjggxx33HHcd9995Obm1narlXYqrTuM/z84cwVMvAG2vg3PToMXT4ZNr4ddnYiIiEideCV8/Cc/uuw7X4b0fDj6v3DKHBh8VtcKmlu2wAMPwCWXQP/+MG0a3HgjdO8Ov/0tLFoES5fCTTfBiScqaLayrtGy2c5cdtllXHnllWRmZvLmm2+GXY7sTmoejP0+jPw6LP2TH7X2+SOhzzEw5nvQ/+Su9R+4iIiItB/VRbDsHlj0OyhfC/nT4OC7oP9JXae7bDwO77wDzzzjp3ff9SPK9uoFJ5/sr708+WTo0SPsSrskc86FXUOzFRQUuNk1wxIHFi1axOjRo0OqqHV0xp+pw4mVwdK7YNEM/5963mg48Ft+kKFoRtjViYiISFdQuhKW/BGW/hlixdDnaH+5T9/jukbIXLsWnnvOh8tnn4Xt2yESgUMOgVNO8dOUKX40WWkVZjbHObfHQU3UsimyN1Ky/EBCI74Gq/4Fi3/nu6u890O/beTXIKNP2FWKiIhIZ7TlHT+Q4eqH/fqQ8/04E519IMPiYnjlFR8wn3/e3wcToF8/OPNMHy5PPBF69gy3TtmJwqbIvoimwbCLYOjnYNMr/j/+D38GC2/0N0g+8BropvswiYiISDMl4rD2cf9ZY/PrkNrN96oa+Q3IHhx2da0jFvNdY59/3gfMt97y2zIz4aij4Atf8OFywoSu0ZLbgSlsijSHmb9XVd9joGgJLL4Jls+EZXf76zlHfBUGfAoi+qcmIiIie6Fii/9M8fGfoGQZZA+DKTf727Sl5oZdXcuKx/1IsS+95KfXXoOiIv85q6AAvvtdHy6nTYMMXbbUkXTqT8DOOayTfNvRGa6t7fTyRsHBd8CEX8DHd8DSO+HVsyBzIOz/JTjgS5A1KOwqRUREpL1yzrdefvwn31U2UQW9j4BJN8Kgz0Ckk1yDmEjAhx/WhctXX/XXXQKMHAkXXggnnADHHaeusR1cpx0gaPny5eTm5tKrV68OHzidc2zdupXi4mKGDRsWdjnSVIkYrHvS/8FY/z//7dyA02HEldDvpM7zB0NERESap6rQ3x9z6Z2wY6HvKjvsEjjgK9B9bNjVNV88Du+/70Pla6/56y+3bPH7hg+HY4+tmwYMCLdWaZIuP0DQoEGDWLNmDZs3bw67lBaRkZHBoEFqFetQIikw6Ew/lSz3I8Z9co+/7iJ7PzjgCt8VJrN/2JWKiIhIW3PO38d76V2w8kGIl0Ovg+GQv8B+5/tBCTuqykqYPbsuXM6a5bvFAuy3H5x2mm+1PPZYGDIk3FqlVXXalk2RdileBWsf862dG1/09+jsdyIM/by/6XJKdtgVioiISGsqWQEr7oPl90LxR/5v/9CLfStmz4PCrm7fbNvmB/F54w14/XV4+22oqPD7xoyBI4/0A/sceSQM7qSDGnUxTW3ZVNgUCUvRx77LzIp7/f2yUnJgyLk+ePY9xgdRERER6fiqdvhrMJff60exB39vzGGX+L/9qXnh1rc3nIOPPvLBsmaquRVJNAqTJvlgedRRcMQRkJ8farnSOrpU2Dxg3AHuwWcfpH9Of/rm9CVFI39KR+ISfjCA5X/39+6sLoKswTD0Iv9HqNvosCsUERGRvZWIwfpn/ZfKa/4D8Qo/mODQz/u/8TlDw66waXbsgHff9a2Vb7/tw+XWrX5fjx5+hNjDD4fDDoOpUyFbvbQ6q7LqMtYXr2dd8TqOGnpU1wmbNsAcXwmWMfrm9KV/Tn8G5A5gQO4A+uf0p39u/3rLfbP7khpNDbdwkYZi5f6azuV/94MKuTh0nwCDz/XffCp4ioiItF+Jatjwom/FXPNvqNwK6b1gvwt9yOw1tX3fF7K6Gj74wIfKd97x88WLfWsmwKhRPlQedpgPmKNGQUQ9sTq6kqoS1hevZ33J+tr5uuJ1dfMgYO6o3FH3oJ/SdcLm6Imj3a8f+nW9F2NdSd3yptJNOOr/nIaRn5VP/9z+tQG0f46f+uX0o39uMM/pT3aavqGREJRvhFX/9K2dm2cBDrqNgcGfDYLn2Pb9B0tERKQriFfBhueDgPkfqNoOKbkw8NOw33nQ/1SIpoVd5c7icd8dds4cP5jP7Nkwdy6Ul/v9vXvDIYf46eCDfatljx7h1ixN5pxja/lWNpRsYH3xej9PCpPJyyVVJTs9Pi2aVq/hrt48tz8nH3By1wmbe7pmszpezabSTfVf4OK6xF6zvrF0I7FEbKfH56Tl1A+h2f3ol7Pz1Du7t7rwSusoW+e/IV31L9j0KuB8V5zB58Lgc6DHJAVPERGRthIrTwqYj0H1Dn/d5cAzYchnof+JEM0Iu8o6iQQsXeoDZU24nDsXSoKQkZXlr7U8+OC6gDl0qD5btENl1WVsLNnIhpINtQGyZjl528aSjVQnqnd6fHZqdqONbQ17gfbI6LHb20d2qWs2W2qAoIRLsLVsa71fXL1vAoJf3IaSDfWbkQOG0Tu7N32z+9Ivpx99c/rSLzuY5/Srt71XZi+ius+i7IvyDf6b01X/gk0v+2s+swbBgE/BwNOh73Ede7h0ERGR9qhsDax9EtY+ARtf8LcqSe3uR5Mf/FnodzxE08OuEqqqYMECmDcP5s/38/feg+Jivz8jwwfLggI/TZkCBx4IKWowCUtlrJKNpRtrc8bG0mBespENpRvqbS+qLNrp8YbRJ7tPbQNYTeNYck/Nmu05aTktUrPCZisrry6vfSPUfosQBNPaN0gwr4hV7PT4iEXondWbvjl96Zvdlz7Zfeib3bd2vW9O3bbe2b1Ja4/dLyR8FZth7X9h3ZN+EIJYif8mte9xQfj8lL+np4iIiOydRBy2vgPrnvAhs/A9vz17qP9yd8Dp0PfYcLvIbtvmr7F87z0fKufN8yPDVgctWtnZMHEiHHSQn6ZOhdGjIVXjlrQm5xwlVSVsKt3EptJNtUGy3rx0o99XsrHRRiyAHhk96jVa9c3uWxsgk6f8rPw2712psNlOOOcoriqu+3YiCKHJb7TkN195rLzR8/TI6OHDZ1II7ZPdp9GpW3q33TZ7SycVr/RdbNcF37qWLPPbu42DAadBvxOg9+Fq9RQREdmVsnW+1XL9c7D+aajcAhb1fz9rehDljW777qVVVX6gng8+gPff99MHH8DatXXH9OnjA+WkSXXhcv/9/e1IpNliiRhbyrbUBsiGU3J43FS6abef6ZMbl2pCZMOekH2y+5Ce0g5ayndBYbMDqvkWpOGbtXY9afvG0o0UVhQ2ep7USCp9svvQO7u3n2c1mGf3rrecm5arcNrZOOdvFL32CT9tmeVHyIuk+T+YfY/34bPnFNB1xiIi0lVV7fCXpGx4Hja8AEWL/Pb0XtD/FN96OeBkSGujgXFiMVi2zHeDXbDAt1IuWACLFvl94Fslx4yB8eNhwgQ/jR8P/fvrGsu9EE/E2Va+jU2lm9hctpnNpZtrl+vNg+1by7c2ep6oRWsvo2vYKJTcOFSzr7P0VlTY7AKq4lW7/Yal4T+e0urSRs+TFk2jd1Zvemf3Jj8r3y8H68nba/b1zOyp6007mlgpbHrNf1u74XnYPt9vT83zXYD6Hg99jwlGuNUQ5iIi0knFSmHLW7DxRR8ut73rxz6IZkGfo/x1l/1O8Lcda82/h1VVPlQuXlw/WC5e7PfVGDrUB8vkUDlqlLrBNqIiVsGWsi1sLt3s58Fn4NrlYL1mvq182053q6jRK7NXvc/Bu+tR2D2jO5Eu+NlJYVN2Ul5dvtO3NMn/GBv+Q2zsAmTwFyH3zOxZF0Cze5OfmU+vrF6125KnXpm96JbRrUv+Q2y3KjbDxpfqwmfJJ357anfofRj0PsJPvaa2r9H0RERE9kb5Rt+7Z9PrsPl12D7X38PaotDrkLpw2evQ1rn2cts2HyBrpiVL/HzZMn/rkRo1oXLsWD+NGeOvrcxpmcFcOprqeDVby7eytWwrW8q27DRtLd9a+xm2JmDuqlElYpGdwmPDhpXknn+9snrp7hJNoLApzVYZq6z9R13zj7nhN0Y185r/DBobYhl8F4OagNorqxe9MnvVBtGakNorsxc9M3vW7u+Z2ZPUqL65axMlK2Dza/4P8ebXYcdCvz2SBj0LgvB5OPQ6GDL7hVqqiIhIoxJxKFoMW9/yf8s2vQ4lS/2+aIYPlzVfpvY+zPfuaQklJfDxx3766KO65Y8/hi1b6o5LS4ORI/3IrzXTqFF+3klDpXOOsuoytpZvZVv5NraWba0NkTWBsXaetG1XDR7gb0lY87kxuVde8ry2MSQrnx4ZPdQjrxUobEqbqxkMqeY/jOSgWvufSyP/oVTFq3Z5zrz0PB9Ag1Bas9wzs2ft1HC9R2YPfSPVXJVbYfMbdeFz27v+mk/wt1npOdW3evaa6sNoWvdQyxURkS7GOShdAVvf9X+jtr4D2+b6UdnBX3NZGyyPgB6T973l0jnfQrlsmZ8++aRu+eOPYf36+scPHAgjRvhp5EjfQnnggb71sgMP1lNeXc628m0+NAbhMXnaWraVbRXBPDhma9lWKuOVuzxnblpuvUaHXlm9dtlbrmZ/Rop6XLUHCpvSITjnKK0uZUvZlka/8Ur+zyr5P7bt5dt32c8e6kJqj4we9YNosN4js8dOyz0ye2iwpF2JlcO2OcEf9GCq+bYYIHdEXQDtMdFf65LeK7x6RUSk83AJKFnubz2yfX5dwKwMBmyJpEGPSUlfhB4CeaP2brCc8nJYuRJWrPDT8uX1Q2VRg5a2/v39SK81obImWO6/v7/dSDsVS8QorChke/l2tldsr/1Mtb1iO9vL/fq2im11y0nT7kJjWjStXgNAr6xe9Myo662W3GsteVtnGSynK1LYlE4t4RLsqNix0zdrW8u21v7nua18W73lmimWiO3yvFGL1guf3TO6++WG68FyzdQjowfdMrp1rRbVym2wbXbSt8rvQvm6uv2ZA4PgGYTPHhN9KO1Kr5GIiOyd6mIo/DAIlu/5eeEHdS2WFvGD2SX3sOk2fs+tljt2wKpVflq9ui5U1kwbN9Y/PjUVhg3z4XH4cD+vmYYNg6xwbiPmnKMiVkFhRWG9qSYsbq/YXi9MNlzfXfdUgOzU7Nov4mu/rM+oW++R0aPRnmZZqVn6sr6LUdgUaURNS2rNN3bJ3+QlLxdWFjb6n/bugir46wiSQ2jN1C29W+PLGd3olt6tdp6RktGx/7Mu35j0AeF9v7xjEbjgdYtm+PuT5Y2GbsE870AfQvXtpohI11FV6P8+FAVTzXLNgHUAqd3qvqzsPtHPu43d+X7RxcX+fpNr18KaNX5avbouXK5a5Y9JlpoK++3nu7Y2NvXvD5GWH9gw4RKUVJWwo2IHOyp3sKNiB4UVheyo3FEbHGu2FVbWX6/5TLK7y48AslKz6JERfEGe/AV6evd6QbJme01Pr+4Z3dv1fR2lfVHYFGlhNUG1Jnw2/FYx+Q9BzXLNH5OabQmX2O1zpEZSGw2heel5teuNLeel59Wu56TltK+Rf+OV/gPE9prwucB/qChbVXeMRSFn/7oAmjsCcg/w2zL763YsIiIdUbzSX1dZvBRKlkHRkrpgWbGh7rhIGuSO9H8Duo33obLHRIj2hQ0b/DWRydOaNXXhcu3anbu4AvTuDUOGwODBfp68PHjwPoXJ6ng1RZVFFFUWsaNyh59X7Khdr7ectL8mTNbs391lQAAZKRk7fTFdEx4bTsnba3pdqWtqx+Kcv4Vq8lRd3bT15O1NfUxTz7Gnx7/6qsKmSLuSHFaTv71s+M1m7bak5eQ/aHv6IwX+gvu89Lx6U26631azr3aetD03PbfePDstu/WCa3UJFC+BHYvrf7Nd/HFdSyhANNOHztz9IeeAYL4/ZA+FrMGQktk69YmIyO45B1XboHQVlC6H4mX+ev6aeekqSP6bldrN92ZJHQquH5T1hMIs2JiAjZt9V9bkULlt287PGY36oDhwoJ8GDao/r5ky/d+GWCJGcWUxxVXFFFUW1S433FZUWVS3HswbThWxij2+JKmR1PpfFCd9edw9fecvk7tl1O/51C2jmwbAwb+1mhKCmhuY9uY5WuocDWtOvgNOW0tJqZtSUxtfT96evPzyywqbIp1OwiUorSrd6RvUxv4oJk87Knfs9Md0T62s4O+pmp2WvVMQzUnL8ctpwXLStpy0nN1OadG03XcVTlT7Dygly+q+Da+ZlyyDeIM/9hl9IGs/yN4PsocE8/18EM0cCBm91TIqIrIv4pVQvh7K10LpyqRpFZQFy7GG9zbMhVgvKMuFwnTYHIE11bC8DFZshS1bfZJoKD0d17cviX59qO6TT2XvnpTld6ckP4+inlls75HB1m5pbM2OUBQvpbiymJKqEoqr6ubJ22rCZFMCIkB6NH2nL2BregzlpeXt9AVuzdSwx1F6NL1VLodxDhKJPQetxtb3Nqy1dKvYvoS4xJ4/orQKsz2HrD2Fs6YEtaYs7+l5m/K4xrZHo3Xz5rxV1Y1WRHbJOUd5rLxeIG34be9O8wZ/yEuqSmrXd3Uj5cZELUpOWg7Zadl+nppdG0STt2WlZpGdmk12WnbdPCWTnlZBz1ghebHt5MS2klm5mbTKDaSUr8XKVmPx8vpPaCn+3qCZAyFzgJ+yguWMfj6sZvT1oTSi+7qKSCfnnA+IlZv8dfaVm3ygLFoJO1ZA6RqoWAexzeCKd354RSoUpsBWYEMMW1cNW/DTJqCs7tjyvExKumVRnJfBjrxUtuWmsiU3wqZsY312grWZMdZkVrEyvYINkTJKY2U7Pd+uRCxS+6VndmoO2am5ZKXmkJ2SS1Y0h8xoLlnRXDKjuWRE/JRpeaSTS7rlkk4uaS6PNPJIiediibRQuiLuTSgLSzS6b2GnqSFqd4Fod6Fsd8fUzBs7Znf7W+Ey3U6rqWFTw0KKdEFmRlZqFlmpWfTL6dfs89W0uBZXFVNaVUpJVUmjU/L+0ur68y1lW1hRuILS6lJKq0oprS5t8jfSNSJmDEnPZkRGGsPTUhmUGmFACvSPVdO3dCm9bSE9qSSbxgdXqIxkU5najarUHsTTepJI7w3pvbD0fKIZfUjJ7EtqZj/SswaQmtnP3xC8Iw/oJCIdX7wat2MNlZuXUr1tOfEda4kVrcOVbsIqtxKJFRJ1O0iJlJKaWk5KehWR1EaajRLADmAbUJg03+6XC0thSxlsiVazNauarZmwNQs/7wtbh8LmbNic5edbMyEeLQdXQYrLJiWRTTQRIRrPIBrPIRLLJlKdg5VlY7Eccquyya3OhqpcqMzBVeXgKnNIVNRN8bIc4hXZxMpyqS7PYEfC2NFWr3MD+xp2UlN9D9/dhaHWahXbl3OkpOjPnDRPu23ZNLNTgJuBKHC3c+7GXR2bm5vrpkyZ0ma1iUjbcDji0TiJaIJ4JE48Gq+3nogm/Lak5UQksfMxwbxmX3o0Tt/0OL3TEvRJwU9R6Bv18z5Rv61vFHru5v7bMQfb4rAjbuyIQ1HM2BGPUBQziqojFMciFFVHKa2OUBqLUlYdoaw6Sll1lPLqFMqqopRXpUAiiiWMSCJCxEX8PBGp21YzuUi948zpE4DInviPOVESiSjOJU8pu1mvW67/uJ2PSbgoziBhjkTEkYgkSJjDRRLEzZGIxHEWJyVSTRqVZNRO5XSzcnqmldIjtZQeqeV0S62gW0oFeWmV5KZWk5taRW56NTnpMbLS42SkJUjPSJCW4UjJgEiWn3YpBhQDRRAvgqpSKC+DknIoqoRtVbC1ylhflcraWDpboxlsT8lkezSbbdEctkdy2R7JYwd5JGK5UJUN1VlQXTPPStqWAdWZWHUGFssgEkvHYulYPIWIJTCLYxYL5rub/DGRSGynbbs7vv56ArNYg3Ps6nG7fp5IZFfPEVIfT5F25JVXXum4LZtmFgVuA04E1gDvmtnjzrmF4VYmIm3JMFLiKdCKF89vsQSbInVBNNFgmWicnIxquqXHyM2ooltGjG5pMbqlxemeFqd7epyclAR5KQnyUhMMTE+Ql+3oFnVk7SaoJitLQGkCSp2fl+1iXuGg3AXzYL0yblTEoTIRoTJhVMeN6kSE6niE6oRRFTeq4xHiiShVMSOWiBCPR4gnIsTjhgXh1pxhzhpfTlhtuK1ZN9f4ttrtSeu7nFBYDotz7DJc7U3Qajy01Z8SiSY8D34YGWdGwsCZI4HVhjiXPEUSOEvUX444sAQuEvPBzqpItSqixEiNVJFqlaRaBamRStKsglQqSbNK0iIVpFFGVrSCzGgFmZFKsiOVZEUqyYpWkxmpIitSTWYkRmYkRkYkRmYkTkYkQWY0TnrEkRF1pEcgPQJpEUiNQmpKMKVCNJgsHUgDMpLmTfwUFi+DeIVRXW5UVkQoKYpSvjlKaWUKRRWpFFaks70yna0VWWyuzGJTZTYbKnPYEsuh1DIptSxiLo1ozRdXcSOaiBJJQMRcEKZ2Dlo9LU6v2rC1GbMNdeErLY6lJ4e79tl4ISLhapdhEzgYWOqc+wTAzB4EzgQaDZujRo3i5ZdfbrvqRESaIl4J1TugusjfqDxWDNXFJKp3EK8sJFa1nVhVIYmqQlKrS8iLlZAXK4V4GRYrw+LlROIVRBIVRBMVRBPVpLhqIiR/q17zAW/fEnm1C6bkZefPFnO+YSTm6pbjDhINJmqWEzUhJmhNajB3ifrbDHw4xSAInxH8vCaM1i47H02Tj8FBBH+BTYS6Y6Du2LpIW7dsEDwfJEfemuOc89sd4D8/W13dwf6al94F+5J/Fc75x9at1xyb9Pia9Qbzmt5GLumxdftrDqrZVjc2dcPHJRVc9zhc7bG+W5wLfuagMnPBTx3ss5rtieC1qtsfMVd7jOHPF6Fm2Z+3dl9wvBEcY/54wxGB4LEE+yFSs25+2ZKXg3nE/LhfRjAP1mu2Ew1OljxvbEoJ9rcQFzMSccPFI7hEColECrFEKtWk4SwdLAOimbhoFma5RKLdiWT0IJqVTySzJ5bdC7LzITcfsntDRk9IySUaiRLFZ9TslitXRGSfNXUgrPYaNgcCq5PW1wCHJB9gZlcAVwAMGTKk7SoTEWmqaDpE+/hBiJJEgmmXwxE5B1VVUF7up7IyP6+o8FN5KVTsgIpiqCyGyhKoKoGqMqguh1g5xCp82I0H80QVJCr9aL+u2t9exsVIdTFSfYzEBXM/ObCaeQIsCB/RpB8gebJGttFgezRpvSb1WXLyTJpocI5dzRv+rWt4TGM0AMQu7XKQ6gaBGmtkW70QnrReu5wc2mvWrUFoT16v+wU7l/zGidSuOyI4iwQJMwKkYBb1A4NFUjBLwSKpEEnFIilEIulYNA2iacG/z1Q/T0n321IyIDXTT2nZkJYF6dmQnuOXUzL8PSGjmcGUkTTPwCxCEzs0iIh0Ce01bDb2MaFe/wzn3F3AXeBHo22LokREaoeer3bESiuJbS8mUVRCfEcJiR3FuOISEkUluJISKC6BkhKstATKSrGyUqy8jEhZKZGKUqLlpUQqyohWlhKtLCNaVU5KVTkp1WVYM6+nr7Y0qiLpVEfSqbKkiTSqyMJfPZZGtUulkjSqXBqVLpWqRCqVLo3KRCrVpFBNKtWkEmtkOUYKcaLESNlpihPdaWpse4IICSI7LUdTIkSihqVEiaREiKYYkZRI7bJFI0RT/TEpqX5fSqoRTfFTzXJKqtUek7w/NZV6x0RTzA+OkWa1g2LUOy7qSEk1Uncz2EY06h+/61EPXaMDcDT8crhJ3xa31DENH7LXj2jd84iISMfWXsPmGmBw0vogYF1ItYjIbjjnb0jc2jdObsq+pj5HvDpBWmUxGRWFZFUVklm1g8yqHWRXF5JVvYPs+A5yY4Vkx3eQEy8iO1FMjisi1xWRRxG5FJNHEdlUN+k1SmCUkUUp2ZSS3WC5f+16OZm1UxlZ9ZYryaA6JZPqlAxi0QxiKXVTPDWYUtJJpGVgaak+GLXgfbzSUyCnje4FFlXTkIiISKfQXsPmu8AIMxsGrAUuAD4XbkkiTZdItNw9utri5srNCYrxVhy8Z0/SonH6pGyjT3QrfaJbybet5Ee2ks9WerCNHm473d12uiW20z2xjbz4dnLj28mJFRJl96MJVqVkUp7WjcqsPCrT8qjKyKUqfRhFmXlsy8ilOiuPWGYe8cwcEtm5xLNycdk5dVNOLuTkQE4OlpW5U4tXeipk7+F+Ycn7dO8vERER6WjaZdh0zsXM7OvA//BX+fzFObcg5LKkGZyrC2DNaZFqzZazlgpr1dXJ1yS1rUhk3+6vFY1CerrPRntz8+SWuo9XbetWvIKMHRtJL9pM2g4/pRZuJmX7ZqLb/BTZuhnbtgW2bMEKC3c9Lk40Cj16+KlnT+iRDz1G1G3r0QO6d/dTt247zdPS0khrq1+ciIiISCfULsMmgHPuKeCpsOtoTc6F36LVliEuLDUhaV+6/2Vl7V33v6Yc0zC8NeUcewqKNdvaZetXdTVs2ADr1/tp3QbYuLHxqaio8XOkpkLv3nXT8KGQnw+9eu16ys3VnahFREREQtRuw+beKCmBV15pH4Fqb5bD7H7Y1JDTsDUrNXXnANYSLV6NnaMpNTYlPCpvtJJ43AfEtWthzRo/X7fOTzXBcv162LKl8cf37Al9+/pp8uS65b59oU8fHyrz8/08L0+/SBEREZEOxlxY/f1akFmBg9nNPEfrhJ09naMlB/BoaqtYu2z9kvalJkiuWuWn1av9PDlYrl+/8zcmKSnQrx/077/zNGCAn/fr5wNkmjqpioiIiHREZjbHOVewp+M6RcvmiBFw5527D20afEMkSWWlD48rVtRNK1fWhco1a3zze7KcHBg0yE/HHw8DB/rl5Hnv3vrHJCIiIiJAJwmbeXlw7LFhVyHSjsTjvvXxk09g2TJYvrwuVC5f7lslk3s1RKM+MA4ZAocd5uc10+DBft6tm7qyioiIiEiTdYqwKdIlVVT4MLl0qQ+Uy5bVhcsVK6Cqqu7YaNSHxqFD4aST/Dx5GjjQN/WLiIiIiLQQfboUac+qq31L5Mcf100ffeTnq1bVb53My4P994cJE+Css/zy8OF+PniwwqSIiIiItCl9+hRpD7ZvhyVLYPHi+tOyZfWvnezWDUaOhMMPh8su8xcsjxjhA2XPnurmKiIiIiLthsKmSFtxzo/wumABLFzo5zWhcuPGuuNSU32AHDsWzj4bRo2qC5X5+QqUIiIiItIhKGyKtIbNm+GDD+oHywULYNu2umN69IAxY+D00+HAA+umoUPV5VVEREREOjx9ohVpjooKWLQI3n/fTx984OfJLZXdu/tWynPP9fOaqW9ftVKKiIiISKelsCnSVBs2wPz5MG+en3/wgR+sJx73+zMyfIg87TQYP95PY8dCv34KlSIiIiLS5ShsijSUSPiBeZKD5bx5PmzWGDoUJk6Ec87xo79OmAAHHOBvMSIiIiIiIgqb0sU554Pl7Nl109y5UFzs96ek+OsqTzoJDjrITxMn+q6xIiIiIiKySwqb0nU4BytW1A+Wc+bAjh1+f3q6D5IXXwxTpsCkSb4bbEZGmFWLiIiIiHRICpvSeRUWwrvvwttv102bN/t9qak+WF54oQ+WBQU+WKamhlqyiIiIiEhnobApnUMsBh9+CG++WRcsFy+u2z96NHzqU3DwwX4aN863ZIqIiIiISKtQ2JSOqbAQ3noL3njDT2+/DSUlfl+fPnDIIb477CGHwNSp0K1bqOWKiIiIiHQ1CpvS/jkHy5fDa6/VhcsFC/z2SMR3h730UjjsMJg2zY8Uq1uNiIiIiIiESmFT2p9EAhYuhFdf9QHz1Vdh3Tq/r3t3HyjPP9+Hy4MPhpycUMsVEREREZGdKWxK+GIxfx/LV1/10+uvw7Ztft+AAXDUUX468kh/G5JIJNx6RURERERkjxQ2pe3F4/Dee/DSS3567TUoKvL7DjgAzjqrLlwOG6YusSIiIiIiHZDCprS+RMJfY/nSS/Dii771cvt2v2/ECLjgAjj2WDj6aOjfP9xaRURERESkRShsSutYvRqee85PL7xQd3/L4cPh7LN9uDzmGBg4MNQyRURERESkdShsSsvYscO3XD7/vA+YH33kt/frByefDCec4APmkCHh1ikiIiIiIm1CYVP2TTwOs2fDM8/46Z13fHfZ7GzfHfarX/UBc+xYXXMpIiIiItIFKWxK023YAP/7nw+Xzz7rR4w187cf+eEP4cQT4dBDIS0t7EpFRERERCRkCpuya7EYvPFGXevlvHl+e9++8OlPwymn+IDZq1e4dYqIiIiISLujsCn1bdvmg+UTT8DTT0NhIUSjcPjh8KtfwamnwoQJuteliIiIiIjslsJmV+ccLFrkw+UTT8CsWf7ayz59/P0uP/Up33rZrVvYlYqIiIiISAeisNkVVVfDa6/BY4/Bf/8Ly5f77QcdBNddB6efDgUFar0UEREREZF9prDZVZSW+sF9/vMf34K5fTtkZPgRY7//fTjtNBg0KOwqRURERESkk1DY7Mw2bfLB8j//8fe+rKiAnj3hjDN8F9kTT/S3KhEREREREWlhCpudzdq18Oij8PDD8Prr/vrL/faDr3zFB8wjjoAU/dpFRERERKR1KXV0BitXwiOP+ID55pt+27hx8H//B5/5jB891izcGkVEREREpEtR2Oyoli3z4fLhh2H2bL/toIPg+uvhnHNg1Khw6xMRERERkS5NYbMjWbUK/vlPP82Z47dNnQq//rUPmPvvH259IiIiIiIiAYXN9m7DBvjXv+DBB+GNN/y2qVNhxgw491x/PaaIiIiIiEg7o7DZHm3d6q/BfPBBeOUVP8jPhAnwq1/B+efD8OFhVygiIiIiIrJbCpvtRXk5/Pe/cN998PTTEIvByJF+kJ/zz4fRo8OuUEREREREpMlCCZtm9lvg00AVsAz4gnOuMNj3A+ByIA5c7Zz7Xxg1tol4HF5+2QfMRx6B4mIYOBCmT4eLLoKJEzWKrIiIiIiIdEhhtWw+B/zAORczs18DPwCuNbMxwAXAWGAA8LyZjXTOxUOqs+U5B++/7wPmAw/AunWQl+evv7z4Yjj6aIhGw65SRERERESkWUIJm865Z5NW3wLODZbPBB50zlUCy81sKXAw8GYbl9jyNm6E+++HmTPhgw8gJQVOPRVuuglOPx0yM8OuUEREREREpMW0h2s2vwj8M1geiA+fNdYE2zqmqip48kn461/hqad8t9lDDoFbb/XXYebnh12hiIiIiIhIq2i1sGlmzwP9Gtl1nXPuseCY64AYcH/Nwxo53u3i/FcAVwAMGTKk2fW2qHnzfAvm/ff7kWX794dvfxsuvRTGjAm7OhERERERkVbXamHTOXfC7vab2aXA6cDxzrmaQLkGGJx02CBg3S7OfxdwF0BBQUGjgbRNbdvmr8P8y1/gvfcgLQ3OOgsuuwxOPNF3mxUREREREekiwhqN9hTgWuBo51xZ0q7HgQfM7Pf4AYJGAO+EUGLTOOdHk737bj+abGUlFBTAbbfBBRdAz55hVygiIiIiIhKKsJrbbgXSgefM39rjLefclc65BWb2ELAQ3732qnY5Eu2GDb6b7D33wNKl0L07fPnL8KUv+duViIiIiIiIdHFhjUZ7wG72XQ9c34blNE08Dv/7H/z5z/Df//r1o4+Gn/wEzjlHo8mKiIiIiIgk0YWEe7Jxo2/BvOsuWLkS+vTxg/1cfjmMHBl2dSIiIiIiIu2SwmZjnINXXoE//QkefRSqq+G442DGDDjjDD/4j4iIiIiIiOySwmaywkL4+999yFy0yF+LedVVcOWVMGpU2NWJiIiIiIh0GAqb4G9Vcuut8MADUFYGU6f6W5icfz5kZYVdnYiIiIiISIfTdcNmLAb/+Q/ccgu8+qof4Odzn4OvfhWmTAm7OhERERERkQ6t64XNLVv8iLJ33AGrV8PQofDb38IXv6j7YoqIiIiIiLSQrhM2583zrZgPPACVlXD88X799NMhGg27OhERERERkU6lc4fNeNzfE/P3v4fXXvPXX37hC/D1r8PYsWFXJyIiIiIi0ml1zrBZUgIzZ8JNN8GyZbDffv62JZdf7keYFRERERERkVbVucLmmjV+VNk77/S3MTn0ULjhBvjMZyClc/2oIiIiIiIi7VnnSGBlZXDxxfDPf0IiAWefDd/6FkybFnZlIiIiIiIiXVLnCJuLFvmRZb/+dbj6ahg2LOyKREREREREurTOETYHDYIPP4Ru3cKuRERERERERIBI2AW0iL59FTRFRERERETakc4RNkVERERERKRdUdgUERERERGRFqewKSIiIiIiIi1OYVNERERERERanMKmiIiIiIiItDiFTREREREREWlxCpsiIiIiIiLS4hQ2RUREREREpMUpbIqIiIiIiEiLU9gUERERERGRFqewKSIiIiIiIi3OnHNh19BsZlYMLAm7Dumw8oEtYRchHZbeP9Icev9Ic+j9I82h9480xyjnXO6eDkppi0rawBLnXEHYRUjHZGaz9f6RfaX3jzSH3j/SHHr/SHPo/SPNYWazm3KcutGKiIiIiIhIi1PYFBERERERkRbXWcLmXWEXIB2a3j/SHHr/SHPo/SPNofePNIfeP9IcTXr/dIoBgkRERERERKR96SwtmyIiIiIiItKOdJqwaWa/MLP3zWy+mT1rZgPCrkk6DjP7rZktDt5D/zaz7mHXJB2HmX3WzBaYWcLMNLKfNImZnWJmS8xsqZl9P+x6pOMws7+Y2SYz+zDsWqTjMbPBZvaSmS0K/nZ9M+yapOMwswwze8fM3gvePz/b7fGdpRutmeU554qC5auBMc65K0MuSzoIMzsJeNE5FzOzXwM4564NuSzpIMxsNJAA7gS+45xr0nDg0nWZWRT4CDgRWAO8C1zonFsYamHSIZjZUUAJ8Hfn3Liw65GOxcz6A/2dc3PNLBeYA5yl/3+kKczMgGznXImZpQKvA990zr3V2PGdpmWzJmgGsoHOkaKlTTjnnnXOxYLVt4BBYdYjHYtzbpFzbknYdUiHcjCw1Dn3iXOuCngQODPkmqSDcM69CmwLuw7pmJxz651zc4PlYmARMDDcqqSjcF5JsJoaTLvMXZ0mbAKY2fVmthq4CPhx2PVIh/VF4OmwixCRTm0gsDppfQ36sCcibczMhgIHAW+HXIp0IGYWNbP5wCbgOefcLt8/HSpsmtnzZvZhI9OZAM6565xzg4H7ga+HW620N3t6/wTHXAfE8O8hkVpNef+I7AVrZJt65IhImzGzHOARYHqDHoIiu+WcizvnJuF7Ah5sZrvszp/SZlW1AOfcCU089AHgSeAnrViOdDB7ev+Y2aXA6cDxrrNczCwtZi/+/xFpijXA4KT1QcC6kGoRkS4muNbuEeB+59yjYdcjHZNzrtDMXgZOARodsKxDtWzujpmNSFo9A1gcVi3S8ZjZKcC1wBnOubKw6xGRTu9dYISZDTOzNOAC4PGQaxKRLiAY4OUeYJFz7vdh1yMdi5n1rrlrg5llAiewm9zVmUajfQQYhR8RciVwpXNubbhVSUdhZkuBdGBrsOktjWYsTWVmnwFuAXoDhcB859zJoRYl7Z6ZnQbcBESBvzjnrg+3IukozOwfwDFAPrAR+Ilz7p5Qi5IOw8yOAF4DPsB/bgb4oXPuqfCqko7CzCYAf8P/7YoADznnfr7L4ztL2BQREREREZH2o9N0oxUREREREZH2Q2FTREREREREWpzCpoiIiIiIiLQ4hU0RERERERFpcQqbIiIiIiIi0uIUNkVEpFMws+vMbIGZvW9m883skGD7dDPLasHnWWFm+c14/GVmdmuwfKWZXdLMeo4xsx1mNs/MlpjZq2Z2ejPON9TMPtdYvSIiInsjJewCREREmsvMpgGnA5Odc5VBGEwLdk8H7gPKQqot6pyLN7bPOfenFnqa15xzpwfPNwn4j5mVO+de2IdzDQU+BzzQQrWJiEgXpZZNERHpDPoDW5xzlQDOuS3OuXVmdjUwAHjJzF4CMLM7zGx20Ar6s5oTBC2WPzOzuWb2gZkdGGzvZWbPBi2HdwKW9Jj/mNmc4FxXJG0vMbOfm9nbwDQz+4KZfWRmrwCHJx33UzP7jpkNCFpja6a4me1nZr3N7BEzezeYah+7K865+cDPga8Hz9HoOYLnvtfMXjSzj83sy8EpbgSODOq4Jtg2wMyeCY77zd79akREpKtS2BQRkc7gWWBwEOhuN7OjAZxzfwTWAcc6544Njr3OOVcATACONrMJSefZ4pybDNwBfCfY9hPgdefcQcDjwJCk47/onJsCFABXm1mvYHs28KFz7hBgGfAzfMg8ERjTsHjn3Drn3CTn3CTgz8AjzrmVwM3AH5xzU4FzgLub+HrMBQ4Mlnd3jgnAp4BpwI/NbADwfXxL6STn3B+C4yYB5wPjgfPNbHAT6xARkS5M3WhFRKTDc86VmNkU4EjgWOCfZvZ959zMRg4/L2iFTMG3iI4B3g/2PRrM5wBnB8tH1Sw75540s+1J57razD4TLA8GRgBbgTjwSLD9EOBl59xmADP7JzCysZ8jaHX8UvBzAJwAjDGrbUzNM7Nc51zxbl4OSGp93dU5guXHnHPlQHnQ8nswUNjI+V5wzu0IalwI7Aes3kMNIiLSxSlsiohIpxBcF/ky8LKZfQBcCsxMPsbMhuFbLKc657ab2UwgI+mQymAep/7fSNfw+czsGHyQm+acKzOzl5POVdHgOs2dHt/I+foD9wBnOOdKgs2R4Pzle3p8AwcBi3Z3jiB8NqxrV3VWJi03fG1EREQapW60IiLS4ZnZKDMbkbRpErAyWC4Galry8oBSYIeZ9QVObcLpXwUuCp7nVKBHsL0bsD0ImgcCh+7i8W8DxwTXfqYCn22k/lTgIeBa59xHSbueJbj2Mjhu0p6KDboF/x9wWxPOcaaZZQTdf48B3qX+6yUiIrLP9M2kiIh0BjnALWbWHYgBS4GaAXvuAp42s/XOuWPNbB6wAPgEmNWEc/8M+IeZzQVeAVYF258BrjSz94ElwFuNPdg5t97Mfgq8CazHX08ZbXDYYcBU4GdJgxadBlwN3BY8Rwo++F7ZyNMcGfxcWcAm4OqkkWh3d453gCfx16H+IhhUaTMQM7P38C3Dyd2GRUREmsyc22PPHhEREelkggBc4pybEXYtIiLSOakbrYiIiIiIiLQ4tWyKiIiIiIhIi1PLpoiIiIiIiLQ4hU0RERERERFpcQqbIiIiIiIi0uIUNkVERERERKTFKWyKiIiIiIhIi1PYFBERERERkRb3/7m6b8ykw9UwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "plt.plot(depth_ns_values, poly4.fit_transform(depth_ns_values.reshape(-1, 1)) [:,0], label='0th', color = 'black')\n",
    "plt.plot(depth_ns_values, poly4.fit_transform(depth_ns_values.reshape(-1, 1)) [:,1], label='1th', color = 'blue')\n",
    "plt.plot(depth_ns_values, poly4.fit_transform(depth_ns_values.reshape(-1, 1)) [:,2], label='2th', color = 'green')\n",
    "plt.plot(depth_ns_values, poly4.fit_transform(depth_ns_values.reshape(-1, 1)) [:,3], label='3th', color = 'red')\n",
    "plt.plot(depth_ns_values, poly4.fit_transform(depth_ns_values.reshape(-1, 1)) [:,4], label='4th', color = 'orange') \n",
    "plt.title('Polynomial Expansion vs Standardized Depth')\n",
    "plt.xlabel('Standardize Depth')\n",
    "plt.ylabel('h(x)')\n",
    "plt.legend(); plt.xlim(-3,3)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also check the arithmetic average of each polynomial basis expansion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The averages of each basis expansion, 0 - 4th order = [1.         0.00536486 0.9458762  0.07336308 2.31077802].\n"
     ]
    }
   ],
   "source": [
    "print('The averages of each basis expansion, 0 - 4th order = ' + str(stats.describe(depth_ns_poly4)[2]) + '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's fit the linear regression model to the polynomial basis expansion.\n",
    "\n",
    "* note the model is quite flexible to fit this complicated / nonlinear data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_poly4 = LinearRegression()                       # instantiate new linear model \n",
    "lin_poly4.fit(df_depth_ns_poly4.iloc[:,1:], Npor_ns)   # train linear model with polynomial expansion, polynomial regression\n",
    "plt.subplot(111)\n",
    "plt.plot(depth_ns_values, lin_poly4.predict(poly4.fit_transform(depth_ns_values.reshape(-1, 1))), label='4th order',color = 'red') \n",
    "plt.plot(depth_ns, Npor_ns, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.title('Standardized Porosity vs Standardized Depth')\n",
    "plt.xlabel('Standardized Depth')\n",
    "plt.ylabel('Standardized Porosity')\n",
    "plt.xlim(-2,2); plt.ylim(-2,2)\n",
    "plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.112467  ,  0.1521794 ,  0.15226789, -0.09614024])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_poly4.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression with Hermite Basis Expansion\n",
    "\n",
    "We can use Hermite polynomials to reduce the correlation between the basis predictor features.\n",
    "\n",
    "* We transformed the predicctor feature, depth, to standard normal since the Hermite poly nomials approach independence over the range of negative infinity to positive infinity under the assumption of standard normal probability density function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>0th</th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "      <th>3rd</th>\n",
       "      <th>4th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.026808</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.026808</td>\n",
       "      <td>3.107951</td>\n",
       "      <td>-2.245605</td>\n",
       "      <td>-4.772444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.780464</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.780464</td>\n",
       "      <td>2.170053</td>\n",
       "      <td>-0.302774</td>\n",
       "      <td>-5.971082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.534121</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.534121</td>\n",
       "      <td>1.353526</td>\n",
       "      <td>0.991769</td>\n",
       "      <td>-5.582071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.356312</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.356312</td>\n",
       "      <td>0.839582</td>\n",
       "      <td>1.573889</td>\n",
       "      <td>-4.653429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.213340</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.213340</td>\n",
       "      <td>0.472193</td>\n",
       "      <td>1.853749</td>\n",
       "      <td>-3.665806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value  0th       1st       2nd       3rd       4th\n",
       "0 -2.026808  1.0 -2.026808  3.107951 -2.245605 -4.772444\n",
       "1 -1.780464  1.0 -1.780464  2.170053 -0.302774 -5.971082\n",
       "2 -1.534121  1.0 -1.534121  1.353526  0.991769 -5.582071\n",
       "3 -1.356312  1.0 -1.356312  0.839582  1.573889 -4.653429\n",
       "4 -1.213340  1.0 -1.213340  0.472193  1.853749 -3.665806"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders4 = [0,1,2,3,4]\n",
    "depth_ns_hermite4 = scipy.special.eval_hermitenorm(orders4, depth_ns.values.reshape(-1, 1), out=None) \n",
    "df_depth_ns_hermite4 = pd.DataFrame({'value':depth_ns.values,'0th': depth_ns_hermite4[:,0],'1st': depth_ns_hermite4[:,1],'2nd': depth_ns_hermite4[:,2], '3rd': depth_ns_hermite4[:,3], '4th': depth_ns_hermite4[:,4]}) # make a new DataFrame from the vectors\n",
    "df_depth_ns_hermite4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: I have omitted orders that had a higher degree of correlation for our dataset.\n",
    "\n",
    "Let's check the correlation between the Hermite predictor features. There is improvement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "      <th>3rd</th>\n",
       "      <th>4th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1st</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059005</td>\n",
       "      <td>-0.346766</td>\n",
       "      <td>0.047248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>0.059005</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.153196</td>\n",
       "      <td>-0.812292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd</th>\n",
       "      <td>-0.346766</td>\n",
       "      <td>0.153196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4th</th>\n",
       "      <td>0.047248</td>\n",
       "      <td>-0.812292</td>\n",
       "      <td>0.015628</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          1st       2nd       3rd       4th\n",
       "1st  1.000000  0.059005 -0.346766  0.047248\n",
       "2nd  0.059005  1.000000  0.153196 -0.812292\n",
       "3rd -0.346766  0.153196  1.000000  0.015628\n",
       "4th  0.047248 -0.812292  0.015628  1.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_depth_ns_hermite4.iloc[:,2:].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pairwise linear correlation is quite low compared to the polynomial basis.\n",
    "\n",
    "Let's visualize the bivariate relationships between our Hermite basis orders. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x27fbdf9eac0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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w2BUKQowimkyjNxAr6rw1Uyu36dq4nfSHcOeOPbjugWdw5449tHI8B4asKeac36l3DEbzUseQIUonslbXu/HS6UFsXk47xEtF9hLcxJPFGitsqPfaUOu25b3tWiCWwnAkCU6b6crasioXukcjSCscEADGgLTKEU6k8cTBXtx2aRPsFnH2ByKkiBIpBX0BBVZZhM8hj2ujVgjTtXKbqo3bUCSBUDw9qaTivszjkKkZcqWYTPbiqUGsqTdOUtxW78FLHUN6h0HyZGe7H3c/+hq6R6PgKoeaPT2hQCeLKSpHfzCOoXCCEmKCO3JKKLjKkUxzpFWAqxw7dnXAH4ojRcfLE4PSkuM4ukdjiCTSRZ/TpiqtGI6kxhL13JKKB55sp9XjGVBSbAJpRcUr50fQ1uDWO5Qxq+vdONgVoF9UJSC7QhxNKpAEBjAGzjkYQ0FOFounFHSPaL88CAGAza2VcFpEMMaQzuQTkjYUcXYogpdODaE/GKc3UMTQEikF/cE4OodjGI4ki9ZffarSCpdVHNsTkpVWVJzwh2lD3gwMWT5BxjvSE0SNywqPTf964iyXVUK9x4ZD3QFcvsSndzhkEbKbNKySgLSi7f6XRAGiwNDktaPWbctbQjwaTWIkmqLkhkyyrMqFo70ByCKDkDnGXlU5JBF4dF8nNrdWYiCcQK3bNssjEaKvtKpiNJrEaDQJmyzCbZPgsmortoUysbTizh17JpVU9AcTYxvyAG1/UDSZxvZdHVRSkUErxSZgtHrirNX1brxMJRSmlruxLq2oUDiHmqmdiKeVvJVNKJnuElQ/TKZzx6YWpFUOcA5FUZFIq0ipHFzhODccAQCE42kEaeMdMZF4Suu7fm4oiuFIEopanPlvym4Vqoo69+TV41fPj1A5RQYlxSbw0ukhtNUZp3Qia3WdG3vo1CnTmrixLrdsQlEBp0XKS9lEtlyCukuQmWxurcSySq3/dW4JBWdaJ4q9mblmKJxEPEXtIIm5qJxjNJpE53AU/lDhW7pNVVKxssYFSbyQ9oXiKXSPxsEYqJwig8onDE5ROV45N4I7Ny/RO5RJVte78f0XzkBROUShcJeFSP5lN9ZFkmlIjEHlgAiM/RwbK+x5SYhD8RQGw7Q6TObmri0X4UuPH4YkcAiMQcm0ZwM47n/iKL709rXY3FqJ/mAcTRX2cb/gCTEDlXOE42mE42lIggCXTYLbJkEuwFieWFJxYf9IGnZZRF8gDgCoc9vGNuMNhuO4+9HX4LHLZXkyHs0oBne0J4hKpwVeA/QnnqjCocV1vC+kdyhkHoq1sW44ksRAiLpLkLnLbrjL9ixWOCAyQBa1Kw4PPnMSezuGte4loUSm1IcQc8rWHneNxDAUThS8tGLi6jEH0FRhGzv/IBRPYTCURDSplO3KMSXFBvfymSGsqTde6URWW70be89QXbGZPPBkO/zBOJRM6yvOMbaxbkmlA5cv8S0qIeZca7c2Gk3mMWpSLpZVuVDtssIqCZBFbdOnqgIqB/qDcdz/xFHs7RjWdvqHqCMFMT/OOQKxFLpGolqyWsAxvbWtFj++6yo8/9nrcfkS37irLQOhBMAAqySAZa7U+INxbHvklbKpN6ak2OBeOD2I1QZOilfWUl2xmexs9+OEPwyVc0iCtiqcVFSoqpqXjXVpRUVPIE7t1sic7DoxgN7R2LjPZTfcJdIqAJ7ZAAoIDBCF8SvGsaSCoQi9+SKlQVE5hsIJdBVpD8bEzXjxtFbnXO2yIhRPoWc0DpVzqJyXzaoxJcUGpqocr5wdMWTniay2ejf2nR2m1RqTyLZfY2CQRBGywMAYkMrDxrpEWkHPaBwJ2gRF5iAYT+Fvf/I6Pvxfe/HvO08hENO6SmxurcQ916+EXRbHVohFpl3NAGewSAIkgeHRfZ3a48RSY3+XkFKQUlT0BeLoDRQ2OZ5YTuG0SKhyWuCxyxgIJcAYwMBgEYWxwz+27+ooWDxGQEmxgbX3heC2yfA5LHqHMq0atxWMAeeGonqHQubgpD8EVVGRUNSxHfyywCAKwEN3XLbghDiaTKN3NI60Soe5kLk5NxiFVRKQUjh+9ko3Pvj9l/G/L59HIqVgc2slvvT2taj12LTuKIK2SKBybeW4ZzSGI72BnI4UCYTp6gQpMbGkdlJe53AUgWiqIDXHueUUD91xGSySiGgyjURaAeccKvjYynHvaAx7zw6XdCkFJcUG9vKZIawx0Cl2U2GMoa3eg71nqITC6Ha2+xGKp8EZgywAYEAqM8murHEtOCEOxFLoC2iX2QiZqw3NXjz76a246w2tcFpFRBIKvrf7DD708F78/nAfrljmG7dizBjAOQDGkNkfOlZGAQD+IPXBJqUppagYiiRwPtPOrVAtCXNXjkVBgCAwNHrtYAzoGY0jpXLYJKGkSykoKTawF08PYZUB+xNPtLLOhZdps53hPfBkO1RVRUrhSKuAxJhWSsEYPnfTmgU95mA4gaFwIs+RknJhk0V84MoleORjV+K9VzRBFhkGw0l87Q/Hcdd/vwIVHH9/8xrUerRT7EQBANdKKrjKx228A7QTE7tHY9THmJQknmnn1jMaQ/doDKF4/jflZVeOt3/wCtS6bZBEBn8wDg7teZwWrZVb92gUdz/6WsklxpQUGxTnHPvODGOtgeuJs2il2PiyG+zYhFVigTG4reK8V4lTioreQAxBquUkeeC1y/g/W1fgB3+xCTes0cbimcEIvvDLw/jJK51496VNY+0Cc1eMJ268A4BkWiuvGAxTyzZSuhKZk/LOD2sn5aWU/Jau5a4aJxQOiyjAZ5cxGksjrXJIAkM0qZTcijElxQZ10h+GTRZR5bLOfmedNfvsGI2l4A/G9Q6FTGP7rg6IDGOrxAzIbLgDVtbN/Y0X5xwjEa2vZqFPZCLlp8FrxxduXoP//ODluGJJBQDg9c4AvvPcaTgtEmpcVogCgygy7RRGziAKDEPhBL70+GF88icHxpLjYCyFzhEtYUjnOWEwqpSiFu0YYWIMinrhpLy+TOeffK0eZ1eNNy+rRL3XhkhSGTsBVVG1g0hKbcWYkmKDernD+PXEWQJjmS4UI3qHQqZx0h9CWuFjq20q1xLkpKLOuQVbWlHRG4hjJEp1m6SwVtW58bXbL8ED79mAi2qcAIDRWAq9wQRiKVXbADS28Y6Dc+02FEmMWzUeSxhGYhgIJUxfVqGqHPGUgnAijWA8hdFoEoPhBPqD2maszuGo6b9HsnDRZBr9wTjODUXhD8YRjKfycrUk27pNa9nGkVJUpFUOgWHcivEfD/eZ/k0ZHfNsUC+eHjJ0f+KJVta68fKZIbz94ga9QyFTiCYVTFwrY9CatM+ldCIQS2EkkqTNdKSoNi2rxBVLfXjqmB8P7z4Df0irX0+rgCRoY1gQtaMYBabVuCfS6rgjoQHtCkconkIonoIkCHBYRdhlEQ6LCMaKd0R99s2kyrWyj0RaS/AlUTuoRFG1JD+lqmP/r3KtzEnl3PQJBykOlXOEE2mEE2kMsSRcVglOqwirJEIU5j/et7bV4j4Adz/6mva7JNPnXhIEpBQVKge6RqL4xGOv41vvvxRvWVef/2+qSCgpNiDOOV4+M4yb1ptnYK2ud+N/Xz6vdxhkCjvb/VOWOgiCtmliJvGUdjgC9R4mehEYw1vX1mHrqhr86vVu/PDFc4ilFKQzCaKoaqcyAtpVkNw643uwciwxzkqrKoIxFcFYCgJjsFtEWERtp70zkyQrKodFWtiFVFXlSGdWsRWVQ+EcybSKWFJZcN2nAkqGycLkviEEAIskwG2VYZEEWCRhzkny1rZaPHTHZfjSrw+jezQGgWXKdTJ9xIXM6+6+3x6FLM5tscWIKCk2oNMDEUgCQ43bpncoc9Za7cT54SiC8RQ8NlnvcEiO7IEdSWX8L1bGgRW1k69G8MwqQyieLttLsbGUgpFIEsORJEaiKQxHkhiNJjEcTWIkoh0WceXySvz9LWv1DrVsWCQB79vYgpvW1+MbfzyB508OggPIDmsxs3KsTDgSOnfFeCKVc0QSaUQyf87toSMKbFzSIAmCtnqbWcVljEHJrOSKmUNwOEfeNzwRkk/JtIqh9IWOQdlxLgkCLKIASWSQMlctUmntTZ3KtZKJZdVOfHzrCtz/xFHEU8rYwToCY0hlXoj+YBwPPNlOSTHJH60/sfG7TuSSRAEral145dwI3rTanC+GUpWtJ55I4cA7L2vCuaEIbLK2WpZSVcSTakkewhFPKRjOSXRHokmM5CS9I9Hk2Md4avbv32On6VMPbpuML9+6Dv3BOL725HG82jkKQEuGsyuquStX060Yz0ZR+Zw3k6pTvL4IMYML43xuYz17sM6Dz5xEfzAOhgv97iWmvdE84Q9jZ7vflIkxzeoG9MKpQVPVE2etqnNhb8cwJcUGM1U9MQBYJIbNyyuhqONXy8wkm+iOZFZwh3MS3dwkdySSQmyBq94Oiwifw4JKp3a6pM9hQZ3XhktbKvL7zZB5qfPY8PX3XYJfvdaN7zx7Crl5KQegKNrHuawYE0LmbnNrJe7BStz/xFFEMm8cBWgLLZwDAuOmXS2mpNhgOOd4uWMYb73FPPXEWavq3HjySJ/eYZAc09YTM8Ahz1xPrJd4SrmQ0EYyK7o5SW8+E12fQ4bPaZmU9FY6LfBl/myb4t/JbZNR4zZ+u8Ry8M7LmtDoteNf/tiO4YhWN5ndj8agjfXFrBgTQibLrhh/4VeHtLKlnDelDDDtajElxQZzZjACgQG1JvyFu6rOjW89dRLxlDJlIkGKb7p6YgHA0ipX0eJIpJRJq7e5SW9u7e5CE127LMLnlFHpsIwluj6HrCW4jgtJbqVz6kSXmNfm1kp85q1tuO+3R7SWbZnPc2idKkQGDIbi+NLjh7GuwYs7NrVQckzIIm1urcRSnx1nhmPjPq9wwCJov38oKc4TxtiNAB4EIAL4Huf8qzqHVBQvdQxhbaO3qG2C8sUmi2j22XGwK4DNy+kXjhHMVE98x6aWRT32VInuSORCfa520/4cXeBBHzZZuJDUZldwp0h0fU4L7JTolrXNrZW495Z1ePCZk+gLxMHYhRVjhWs3gXEMhuO0akwItCvT8ZQ61r4tMvZRGf/nZBrhuPbnSFIZd//p9l+kFY6T/lCRv6PFW3RSzBi7lnP+wmyfm+djigC+A+AtALoA7GOMPc45P7q4aI3PrPXEWavr3dh7ZoiSYoNIplUwAZhYVGyR2JQJwbSJ7lgJQx4SXUnIKVkYv4I7royBEl0yT7m1jvGUApvEkEzzseGvcqB7VDt5877fHsG9t6yjxJiYVlpRLySwmcQ1nMxJasf+PDnZzX6uUK2vmaD9/jGbfKwU/xuAy+fwufnYDOAU57wDABhjjwK4DUBJJ8VmrifOWlXnxp6OYfzN9XpHQgBtTE3VIUpVgQefPjmW9GYT3kgeEl2fUx63uptb0lDpsMA+S29kQhYjd3e8JDD0jMYgIbMJCBgrrYimVHztj8fxd29dTYkxKTrOOWIpRVuBTSpjSeqFpHXCam1uYpvUEt54AZJOWWSZwz60W/bgD5dVgtMiwWXL/SjCZZPw+Z8fRDw9PrtWVJjy5NMFJ8WMsasBXAOghjH2yZwveaCVPCxGE4DOnD93AbhywvPfBeAuAFiyZMkin84YTg9EIArMlPXEWavr3di+6zTSigpJLM9TxI00NhljYMCk1v8plePXr/fM+HcnJbpjSS4lumZkpHFZaNkV40f3daIvqJVSMJWPHXGeXR0biiTxxV8dwj3Xr8QtlzbqGnO5Muu4TCnq9OUGOZ8b+3MyjXD8wqpupACrtAyAwyqOJa3jE1nxQkJrvfC5bOKb/dpCDq0RBQETW7oxwJRloItZKbYAcGUeI/d6fxDAexcTFLR/z4nGDR/O+Q4AOwBg48aN5ns7MgWtnthjyoGU5bHJqHZZ0d4Xwvomr97h6MJIY5NzPuVZWAIDrm6tGktqfU75wv9nShko0S0tRhqXxbC5tRKbWyuxt2N4rKeqKGiH1uRSOPDNp09i9+lBfPptq1HtMu+ihBmZaVz+6KWz+LdnTiEYSyFRgFVaiyTAaRHhtslwZpLbsaTVKo6t3jozf3aNW82V4LCIEPTIH6ZYEeYos5VizvlzAJ5jjP2Ac34OABhjAgAX5zy4yLi6AOTuAmoGMPOyVgl44eQg2urNdWjHVNrq3Hj5zHDZJsVGwhiDkLPhKMsiCbj/nev1CYqQIppYZ5w9hUsUWOYYZu1+e8+O4I4de7B1VQ0+8ZZVcFoNuw+d6CStcAyEElN+jQHjyg0uJLPZRFYc/+fcldtFrNIawcSiO22VuPxWirP+mTH219D+XV4B4GWMfZNz/rVFPOY+ACsZY8sBdAO4A8AHFh+qcakqx56OIbz94ga9Q1m0VfVuvHh6EB+7brneoZQ9WdSOn524XBxPqdjbMUy1lKQsTDyFSxAuLG5lf4FnyyqeOT6Al88M46PXLcctFzdALtMyMDLZllU1eOA9G5BW+KQk167XKq3O9nYMT+pAwaFdjbSI5vv3yEdSvJZzHmSM/RmA3wH4LLTkeMFJMec8zRj7GwB/gFaf/DDn/EgeYjWsE/4QHBaxJC7dtdV78KOXzkFVOQTBfC+KUrKqzoP9Z4egYnxeLAvAo/s6KSkmhieLQubGIAkCWDahzQzopKIipahIK3zG48knrhhbJIa0kpmjOCAyPtbXOJJU8G/PnMLPX+3CX163HG9cVWPKVS+SXytqXWjw2tAfjOsdSkEIjEGWBEgCgygwyIIAiyRkFlcYOOdIKiqSaRXxlPa62/F8x6R9K4xpvfBX1pnvync+kmKZMSYDeCeAb3POU/mYPDjnv4OWZJeFF04NYV2T+QbQVCqdFjitIk76w6ZuL1cKtm1pxctnhibVFadV4NywGQ92JqUmu8LGwcHAYJUFWEQBjCHzce6/T7K/tNMKRzTTTzW3rnGqzhQCONTM8bSiyCAJWscWDqBnNI77fnsMq+u78NdbWnEJHe1NSowoMNgt2qq3XRZneb0xSKIAh0X70852P84ORSYlxSLTuoBu29JauMALJB9J8XYAZwEcALCLMbYUQCAPj1tWdp8cwIYSqsFtq3djT8cQJcU629pWC6skIJ5zyhegTWATT47LToZm3BxBzKvOY8vbYzHGYJVEWCWtvrPKaUEspSCeUhCKp6FyPmVnCkHlgMAgMIZ0pgA/e0S0woHjfSF84rEDuKq1En/1hlYsr3bmLWZC9GC3iKiwL3xD9c52P+5+9DWoXHutiNoFl7EypFW1LtOdZgfkISnmnD8E4KHsnxlj5wH8aLGPW07Siop9Z0fw/k3maUczm7Z6D148PYiPXLNM71DKnsMiIqmok/oVx1MqzgxE8MbVNVA5xi6RpRRVawKfSCM1VZNjQkxCENjYbn2fw4JATOvJPWVnCsaRVrSNdyIDBEHr513ltMBplXB+OIo9HcPYe2YYb1tXjz+/ZhlqTNw+k5QfgbGxVm2LOep+Z7sf9z5+BNGkAknQrjwqHJAFBkkAFM7xuZvW5DHy4sn7DgKuLTN9Kd+PW8oOdgdQ67bCa5f1DiVv1jR4sPfMMK06GsCqOs+UL3SLyPC93WcgiVrdWHalWBa1/sQtlQ7Ue21wWGgXPjE/QWDwOS2o99ogZvY6bG6txD3Xr4RdFqGqGOtMIYkCwLW6ylA8hZ7RKJZVOVDtskDlwO8P9+FDD+/Fd5/vQDie1vk7I2RmFklAtduKpVUOVLusi06I7370NXSPRqFyDoBBFrQN3enMPqKVNeZcJQYWkRQzxg5OczsEoC6PMZa83ScHsbaxNOqJs6pdVlglEaf8Yb1DKXvbtrSOtZ3KNZez6R0WCfVeG+o8NtqFT0qCwyKhqcIOayYxyNYZ13psWimFoHUDUrm2cpx9Y6+oHJLA8M5LG+FzyEimVfx4byc++P2X8bNXukx5pC0pbVZZRJ3HhmafAx6bvOjNouNXiLV2n+nMoTgWkUEQgFq3zbSrxMDiVorrAHwYwDumuA0tPrTysevEANY2lFZSDABrGz3Y00FDQW/ZuuKJ06EKIDrHY52dVi2RoAM9SCmQRAGNXhtcNu0qyMQVY0nUfuGLIgOg1RoPhhPwhxL409F+/O2bV+IjVy+FTRYQjKfx7ztP4y9+sA9PH/NnVs8I0Y8oMFS5rGiqsOet3/bEFWJFBWRRhCRg7M9Oi4T7bl1n2lViYHFJ8W+hHdRxbsLtLICdeYmuDESTaRzpCWJNCSbFq+vc2H1qUO8wCLS6YmGKV3ssqWBnu39OjyEIDPUeG1x0qAEpAYwx1LptqHFbITA2bsW42mXVDrzhF1aM0yqHKADxlIL/3NWBNfUePPKxK3HbJY0QBYbeQBz/9Ltj+P8eeRWvnhvR+9sjZUgStDKJJZWOvJZjTrdCnFJUiJnSicYKOx664zJTJ8TAIpJizvnHOOe7p/laSR+0kU8vnxnGRTXORdX4GNW6RqorNopsXfHEq2eMAQ882T7nx2GModZjQ0W2Jw8hJue2yWissEMWhbEV4yqnFYLAwIQLK8YCY2N1x/3BOO5/4ihO9Ydxzw0r8fBHNmLLymoAwEl/GJ/+2UF87ucHcZrKx0gRyKKAKpcVLZX2vJRJZO1s9+POHXuw7ZFX4A/GIQkM4KwkV4izqEhQZ8+fGCi5euKsKpcVdlnEiX76xaC3bVtaoULbRJSLATjhD895tTir0mnJ1GDSgQbE/CySgHqvbSwx/ub7L8H9t65HldOaqcfnSCsqFK1z29iK8YPPnMTejmG0VDrw5VvX4dt3XoYNmX7ze8+O4K7/fgVf/X17yR72QPRlk0XUuK1o9tnhtecvGQYurA77Q3GomSsmKUWFwjlUlZfcCnEWJcU623ViEBuaKvQOo2DWNmqt2Yi+trbVYmWNC2rO0baA1kaHcz6v1eIsl1VCg5c24JHSIIvCnDtTTFwx3tsxDECb7771/ktx/23rsLTSAQ7gj0f78eGH92L7c6cRiqd0/A5JqbDKIhq8djRW2OHO48owMHl1WFG5dohO5uAO7TXASm6FOIt+m+moPxhHfyiO1hJuBN9W78HzJykpNoLP3bQGjAGSMOFITixstRjQViqafXYqpyAlQRYF1HnGJ8YTO1PMtGIMaCVG166oxvc+shGffMsqVDktSCkcP9nfhQ9+fy9+sq+TOlWQBXFYJDRW2Au26Xmq1eGe0TicFhEqtE4sCueo99pKboU4i5JiHT1/chAbmrwQhNK9BL2u0YN9Z4eRpkMgdDdxtVhg2Wbr2tn223d1LOhxGWOodFrQWGEfSyYIMStbZhVurr2MLZIASWB4dF/nuMcRBYZbLm7Ajz62GR+9dhkcFhGheBrbd3Xgww/vxR+P9FGnCjInLquEZp/WN76Q+4+27+qALDI4LNLY6jBjQCSpoNFrhyAwiIKAWret5FaIsygp1tGz7X6sayydo52nUuGwoNJpweGeoN6hEFxYLbaIDBZRAAeQVFQkUypePT+yoNXiLJssojGn/yshZmWRtBVjgc28YpxSOeIpFQOhOM4NR6Z8LLss4oNXLcUjH9uMd1/WBElg8IcS+OqTx7Htv1/BvrPDxfzWiEkwxuC2yWj2OVDrscEiFSZdy5ZLXPfAM3j1/MjYAla1yzq2OpxIK5BErVvL9g9egR/fdVVJJsQAJcW6UVSOF04N4uLm0k6KAWBtgwcvnBrQOwyCC6vFgsCQUjmUTJspSdRWBO59/MiiEmM52/+V2rYRk7PJIuq9FzaT5q4YKwrGjoOWJYa0yhFOpMdKKKZS4bDgb65fgf/6i0140+oaAMDpgQg++/ND+LufHsDJ/pkP0iHlQWAMXruMFp8dNW5rwZJhYHy5RIVdBmNA92gcoXgKHrtcNqvDuSgp1snh7gA8dhnVLqveoRTcukYvdp2gumKj+NxNa1DrtkEWGGSRQWQCVA5wlaN7NIq7H31tUYlxtm2bp4SOLSflKbu7Pyu7YiyKDJKglUgoCkdaBVIKH7fpbjpNFXZ86Za1+Pc/uwyXtmiLIq+cH8W2R17FPz1xDL2BWEG/J2JMAmOocFjQUulAlcuqlecUQO7K8N2PvoaUosBhkcAYQ53bBgDoC8TBOS+b1eFclBTrZOdxPzY0lf4qMQCsaXDjYHcA8dTcTk8jhbW1rRb33boOHNqGO8a0DhRgDJLAEE0qi14xBrTLbz7agEdMzmWVxo3jza2VcFpEyKKAtMovrBiLkzfdzaSt3oNv3H4JvvKu9Vie2Wz9dLsff/5f+/DvO08hEKNOFeWAZVeGKx2odFoKui9j4spwNKlgMJQc64risctoqrCBAwjEUmWzOpyLkmKdPNPuL4vSCUDbMbu8yom9Z6h2zii2ttXi8iU+LKl0QBS0VjsC0xqzW6XFbbzL5XNaUO22Uj9jYmo+p2XclY9lVS5Uu6xjr5XZNt1NhzGGq1qrsONDV+Dv3rYa1S6tU8XPXunGB7//Mv735fNI0GJCydI20NlR5bIWZZNy7kY6xrS5HgwYCCXG7iOJAi5f4sPzn72+bFaHc1FSrIORSBKn/OGSPNp5OmsbPXjuBNUVG8m2La1IKRzxtAJAa8iucO2QgnNDkUVvvMvy2GTU57S5IsSMql3WsTZYd2xqQVrlSKRVZA/2mMumu+mIAsNN6+vx3x/djL+8bjmcVhGRhILv7T6DDz+8D78/3AdFpU4VpcIiCWissKPWU9g+77mlEnfu2IOT/hDsORuhs+Wb8bQCzjmiyTRSCse2La0Fi8noKCnWwa6T2il25XTowYYmL3ZRUmwo2TIKp0WCoo4voxBYfjbeZdkt4riDEQgxo1q3bdxx0AvddDcdqyziA1cuwSMfuxLvvaIJssgwEE7ga384jrv++xXs6RjSXqPElESBodptRbPPUdDWasDkUgl/KI5QPI2hyIVVYY9dRpXTAqdFKttyiYloi7gOnjraj4ubK/QOo6guqnGhLxhHfzCOOo9N73BIxta2Wjx0x2Xa5BmMa0kr13qxspyNd/lo0m6VtP6v/cE4UtS3mpiQKDDUeWzoGY2Nbbr70uOHIXAOcCCZ1hJWiXHseL4Dm1srF/Q8XruM/7N1Bd59WTO+v/sMnm7348xgBF/45WFc2uLFXVta0VZfPlcazS7bUcJrlwt2LsHOdj+27+pA50gULT4HRqPJsVIJQCtj9DlkDEdScFgk2GURsZQCiyTiq+++uKwT4Vzls1RpEIrKsevkIC5tqdA7lKISBYb1tFpsSMXaeAdolw2bfXY4qWUbMalsD2PG2NimO8YYMvkwJKa9hs4ORRa0Wpyr3mvDF9++Bv/5wctx+ZIKAMDrnQH8n/95Dff/9ii6R6lThZHlbqLzOS0FTYgnrgqf8IcnHZpV7bLCbRVR67bRyvA06DdTkb12fgSVTktZtGKbaEOjF8+2+3H7xha9QyETZDfe+UNxrR0PtI13qgpYJTa28S4fkydj2mrbYDiBIO2wJyZkt4ioclkwGEpgWZULR3sDkEU2dtiHqnJIIvDovs4FrxbnWlXnxtfeezH2nxvBjl0dOD0QwbPHB/D8yUG845JGfOiqJXTUusG4rBJ8TktRyiRzN9AB2qqwLDL0BxPw2C+Mi1hKwco6D35811UFj8msaKW4yJ461o9LyqTrxESXtFRg96lBOvLZoKbaeKeCaxuMZBFdI9G8Pl+1y0q/yIlpeWwyPHZ5bNMduHb6l6pycHBUOy3oC+ZvJZcxhk3LKrH9Q1fg8ze1odZtRVrl+OVr3fjg9/fikT3nEKNOFbor1ia6XJ0j0XEb6ACgzm1FSlURTaZpE908GC4pZozdzhg7whhTGWMb9Y4n35466sdlS3x6h6GL7Ar5652jeodCpjBx450kMjR67WAMOOUPwx9K4M4de/JSRpFVSS3biIlVu6x405paLKt0gAnaJru0yqGowGA4ObZyl08CY3jL2jr86KObsW1LK9w2CdGkgodfOIsPf38vfvZKFy086EAWBdQUeBPdxG4S2bm4xeeY9IZIEgWsrHFRqcQ8GS4pBnAYwLsB7NI7kHzrHI5iMJLAihqX3qHo5pJmL54+lr+kiuRXduNdY4Ud9V4bAI6ukRjSKke9xwp/KJ63+uKsbMs2gRJjYkK1bis+/qYVcFokMACSoN0UlWMkmlx0XfF0LJKA929qwSMf24z3b2yGLDIMRZL4h8eP4Au/PFSQ5ySTWWURdR4bWiodcNsKd4rnVHXD2bk4e5Vv4qrw525agx/fdVXZ9hxeCMMlxZzzY5zz43rHUQhPHevHZS0VBSu2N4NLl/jw1LF+vcMgM8iuGNe6begLJiCJDE0VdnjslrFatXwc7JHLbhHRUGGDJBhuSiJkRowxvPPyZvgcMkSRgUNbpavz2OCySnM+yGOh3DYZ2954EX700c1427o6CAz4syuXFvQ5ifampN5rQ1NFcTYOTzx4I3cuzp2zaVV4cUy70Y4xdheAuwBgyZIlOkczN08e7sO1K6r1DkNXK2pc8IcS6BqJotnn0DucgjDj2Jxoa1sttrbV4roHnkGFXR5X3pBWVLx6fgTXPfAMWnwObNvSmpfJ1yqJaKywoS8YRzJNl3/zrRTGpVGJAkMgloIAIDtyGQNsspDXuuKZ1Hls+OyNbfj0W1djZZ27KM+ZD2Ybl6LAUJt5w1MoE9urbdvSis6RKCrs41eic/d6ZOdssji6LMswxp5ijB2e4nbbXB+Dc76Dc76Rc76xpqamkOHmRSCawqHuADY0lecmuyxBYLispaKkSyjMNjZnMrFWLRRPoXs0DsYw6RJePkiigEavfezkMJI/pTQujWZnux/hhIK0yiFkaov9wQRGo0nUe+xFjaWxorjPt1hmG5c2WSx4QjxVmYTbKk2qG46llJJdXNKLLkkx5/wGzvn6KW6/1iOeYnj2uB8bmrwFP8XGDC5dUoEnj/TpHQaZg4m1an2BOACgzm2bdAkvXwSBod5jK2h9HiH5tH1XB3yOzBUVFWAAODhGY2ncsYlaUJK5m65MgnM+Zd0wdZPILyrgK5LfHeotuwM7pnNJcwUOdI4iGKcetUY3sVaNA2iqsMGTcxmvEO3aGGOocVupMwUxhc6RKKpdVjR67ZAlASrnkEUBLouYlz7FpHxM1V7NLouIJBWqGy4Cw9UUM8beBeDfANQAeIIx9jrn/G06h7Uo8ZSCF04P4j1XNOsdiiHYZBHrGj14tt2P2y5t0jscMovcWrU7d+yBPxQf9/VYSoHLKuHOHXvG1cDlY7L22GRYRAH+YAJpleqMiTG5rRJO+cNQOIdFFNBUYQcHUOkov0OayOymqhnOzpctPgf8ofi4dn7ZMgmqGy48w60Uc85/yTlv5pxbOed1Zk+IAWDXiQFcVOOChy4Hj7l8iQ9PHOzVOwwyT1O1/gnGUhgIJ6ZsFZQPNllEk4/qjIkx7Wz3YyCcQFrlYABSioru0TiiiTTu2EylE2S8mVqrAVPPsVQmUTyGS4pL0e8O9eKylvI8sGM6ly/14YXTg4gl6QQmM5mq9U+V0wKvXZ6yVVC+iAJDg9cOr53eWBJj2b6rA167jKYKO2RRyLRkY6h2WXHT+ga9wyMGM1NrNWDqOZbKJIrHcOUTpSalqHim3Y9/fvfFeodiKB6bjJW1buw87sdNG+gXh5lMvISXbduWqxB1xgBQ5bLCKosYDCWgcp73xydkvrKtspiFjdXac84RiKXgdchIKArC8bTOURKjmK21GkDt1fRESXGBvXh6CI0VdlQ6LXqHYjhXLPXhNwd6KCk2uZlq4GaqnVsol1WCRRTQH4wjRcfZEp1kx/ZAKIHBUAL13gsdU3JbZdW4rEimVeq9XUYWWjNM9EflEwX2mwM92LSMdh9PZfPySuw6OYhoklZRzGy6GrirWytnrJ1bDIskFO0kKUImyq0LrfdYkVa149CDseSkGlDGtBaDYhmfZFpOqGbY3CgpLqCUouJPR/spKZ6GVkLhwlMlfJBHOZiuBu6ljuEZa+cWSxAY6jw2ugpDii63LtRjt6Cpwg5JZOgLJqasAc0e+0ztBUsf1QybGy2zFNCLp4fQ4LWhxk1teaZzZWsVfvVaN269pFHvUMgiTFUD9/e/PlyUWuMKhwVWSYQ/FIeiUp0xKbyJdaEeuwy3TUIglsKP77pqyr9jk0VUOiwYiiSKFSbRAdUMmxutFBfQ46930yrxLDYt8+HljiGMRJJ6h0LybOIR0UDhaufsFhFNFXZY6cRIUgQLHdtehwyXjdaiSlkx5z2Sf5QUF0gireCPR/txVWuV3qEYmsMi4dIlFfjtwR69QyF5VuzaOUkU0Oi1UdJBCm4xY7vGZYVFol+9pYpqhs2NfnsUyK4Tg1hS6aB6xzm49qJq/GR/Jz509TK9QyF5tLWtFvdBq7HrGomieYruE/nuTsEYQ63bBklIYjRKVx/Iws00NucytqeT3XjXPRqjch8Tm258LGZsEP1RUlwgv3ytC1cup1Xiubi4uQLf330Gp/whrKh16x0OyaOZaueyu7RlkY3bpX1f5u8tRqXTAlFgGApT/SaZv7mMzcXUhUqigFq3Db2BWB6jJsUy2/igmmHzoms4BRBJpLHrxCCubKV64rkQBYY3rKzGo/s69Q6FFNFsu7QXy2uXUUs7/skCFHpsAlodfJWTNmGbUTHGB9EHJcUF8MejfWird8NjoyNp52rLqhr8/JUuJNJ07HO56ByJwj5hY1y+u1O4rBLqPTYIlBiTeSjG2AQyG++o17bpFGt8kOKjpLgAfvZKF22wm6cGrx0tPgf+eKRf71BIkRRrl7bdIqKhwgZZpOmOzE0xOwhUu6w0Nk2GOkyULnol5pk/GMeBzgA2LvPpHYrpvKmtFj986azeYZAiKeYubaskorHCDhu1bCNzUMyxmT2Ehq5mmAd1mChdlBTn2a9e78amZT5YJfrlO18bl/pwZiCC430hvUMhRVDsk51EgaHBa4PHTmVNZGbFHpsWSUA1HfJkGnQqXemiYqY84pzjsf1duHPzEr1DMSVJFPCmtlo8/MIZPPCei/UOhxTBQndpL7SVG2NMu1wtCHSyWIlbbLu/YncQcFklxO0ygrFU0Z6TzE++W0gS46GV4jw63B1EOJ5GWz21FVuoN7fV4omDvRimE+7INLLtkPyh+Lh2SDvb/XN+DK+DOlOUsnyMET1UOS10KqNBmXVMkfmhpDiPHt13HltWVVNt2CJUOCzYvNyHR146p3coxKDy1Q7JZZVQ57FSYlyCzNoyizGGOrcVokBj0mjMOqbI/FBSnCexpILfHOjBG1bW6B2K6d24rgE/eOks4ilqz0Ymy2c7JIeFEuNSZOaWWdmDPYixmHlMkbmjpDhPfn+4Fytr3ah20WaJxWqpdOCiGiceo8M8yBTy3Q6JEuPSY/aWWXaLiEqnRe8wSA6zjykyN5QU58l/v3QOb1xFq8T5csvFjfiP504jpah6h0IMphDtkCgxLi2l0DKrwmGBw0J74Y2iFMYUmR0lxXnQ3hfE+eEoLltaoXcoJWNVnRs1bit+8WqX3qEQgylUOyRKjEtHqbTMqnFbIQn0a9oISmVMkZkZ7m0oY+xrAN4BIAngNIC/4JyP6hrULH744lm8aXUNTV559q7LmvDgUyfxrsuaYZHo35ZcUKh2WQ6LhAYvQ18gDpXzvD8+KZ5it1QrBFFgqPVY0RuIg9N41F0pjCkyM8MlxQD+BODznPM0Y+wBAJ8H8FmdY5pWMJ7Cbw/24qvvpr66+dZW70G914af7DuPD129TO9wSImYrdeoTdaOhe4LxKGolIgUCvV8nRubLMLnkKlNpUnQuDY3wy2/cc7/yDlPZ/64B0CznvHM5rF9nbikuYI2RRTIe69owYNPn0QkkZ79zoTMYq69RrPHQsui4abIkkA9X+eH6ovNgca1+Rl9xv8ogN9P9QXG2F2Msf2Msf0DAwNFDkuTVlQ8vPsM3rauXpfnLwfLq51oq/dgh4l6QRphbJKpzafXqCwKaPDaSqZ0x0jjknq+zl+p1hcbaVwuFo1r89PlFcYYe4oxdniK22059/kigDSA/5nqMTjnOzjnGznnG2tq9On68OSRPlQ4LFhR69Ll+cvF+zY2479eOIO+QFzvUObECGOTTG2+vUYlUUCj1w5bCZwyZqRxST1f5y9bX1xqG0GNNC4Xi8a1+emSFHPOb+Ccr5/i9msAYIx9BMAtAP6MG3R3Aecc33n2FG7e0KB3KCWvxm3Dm9fU4R+fOKp3KMTkFtJrVBAYGrw2unydR9TzdWGy9cXEmGhcm5/hrsUwxm6EtrHuVs65Yd9e7TwxgGhCwWVLKvQOpSzcekkjXj4zjJdOD+kdCjGxhfYaZYyhzmOFy0aJcT5Qz9eFo/pi46JxbX6GS4oBfBuAG8CfGGOvM8b+U++AJuKc41//dAK3XtoIocQuZRmVTRbxwSuX4gu/PIREmo5/JguzmF6jjDHUum3w2mmlbrGo5+vilGp9sdnRuDY/w73d5Jyv0DuG2ew8PoBANIWrllfpHUpZ2bTMhxdPD+Lfnj6FT79ttd7hEJNabK/RKpcVyTSdtLhY1PN14ah/sXHRuDY3eqs5T4rK8ZXfHcN7rmiGINAqcTExxvDhq5fhkT3ncLg7oHc4pIyVSkcKYl42WUSlg1qBEpJPNLPP089f6YIkMGxc6tM7lLJU6bTgA1cuwd2PvoZ4isooCCHly+uQ4bQa7oIvIaZFSfE8BOMp/Msf2vFnVy0tubY4ZnLdimo0eO34f785oncohBCiqxqXlQ6ZISRP6JU0Dw/8vh2XNFfgohrqS6wnxhg+eu0y7Dw+gF+/3q13OIQQohtBYKhxl17/YkL0QEnxHL3cMYQnD/fhjs1L9A6FAHBYJNzz5pW499dHcKSH6osJIeXLJouodFJ9MSGLRUnxHARiKXzisdfxF9cuh4vqtwxjaZUTf37NMnz0B/vQG4jpHQ4hhOjGa5epjzYhi0RJ8SxUleNTj72ODU1eXEGb6wznqtYqvLmtDh/83ssYjiT1DocQQnRD9cWELA69embx9T8eR9dIDH925VK9QyHTuOXiBmxo8uLOHXswFE7oHQ4hhOiCMUbtAglZBHr1zOC7uzrwq9e68YkbVtG7bwNjjOF9G1uwrsmDd//Hizg3FNE7JEIIIYSYDGV6U8ge4/zwC2fw+ZvXwEPHuhoeYwy3X9GCN6+pxTu/8wKebffrHRIhhBBCTISq8icYiSTxmZ8dxLnhCO69ZS0q6MQgU3nLmno0VzjwmZ8fxA1ravG5m9bAS29qCCGEEDILWinOSKZV/M/L5/Dmbz4HiyTg799OCbFZrWnw4J/ftQGD4SS2fu1Z/OdzpxGKp/QOixBCCCEGVtYrxZxztPeF8LuDvfjJ/k40VtjxqbesQisdzmF6TquEj167HG9ZU4fHD/TgO8+cwlvW1uHmDQ24+qIqOhqVEEIIIeOUfGYQiqdwdjCKUDyF4WgS/mAC54ejONEfwqHuAGySgMuX+nD3m1diaaUDAJBWVJ2jJvnS4LVh25ZWjERT2NMxhG/86ThOPhJGS6UDa+rdWF7jRIPXjmqXFRUOGS6rhGqXFfVem96hE0IIIaSIGOdc7xgWjTE2AODcVF+rfNvHm92X3lQ33d/lqpL/fwDOGRgz7j+skeMrQmxMEGc9D/XcA7e8Ms2XbJzz9XN+rhnG5gJUAxjM02NRDKUVwyDn/Ma5/sXMuIxAv+9Bz38/eu7imvPYnMd8aYTX33SMHBtA8WVNOS5LIik2GsbYfs75Rr3jmI6R4zNybIC+8Rnh34ZiKJ0YynUs03Obn5G/FyPHBlB8s6GNdoQQQgghpOxRUkwIIYQQQsoeJcWFsUPvAGZh5PiMHBugb3xG+LehGDSlEEO5jmV6bvMz8vdi5NgAim9GVFNMCCGEEELKHq0UE0IIIYSQskdJMSGEEEIIKXslkRTfeOONHADd6FaM27zQ2KRbkW7zQuOSbkW8zRmNS7oV8TalkkiKBweN3IealDMam8SIaFwSI6JxSfRWEkkxIYQQQgghi0FJMSGEEEIIKXuUFBNCCCGEkLIn6R3AdBhjIoD9ALo557foHQ8pvp3tfmzf1YHOkShafA5s29KKrW21eodFCCGGQ/Nl6aOfceEZeaX4HgDH9A6C6GNnux/3Pn4E/lAcFXYZ/lAc9z5+BDvb/XqHRgghhkLzZemjn3FxGDIpZow1A3g7gO/pHQvRx/ZdHZBFBodFAmPaR1lk2L6rQ+/QCCHEUGi+LH30My4OQybFAL4F4DMA1OnuwBi7izG2nzG2f2BgoGiBkeLoHInCLovjPmeXRXSNRHWKaO7MMjaHwgl85OG9uPnB5/F656je4ZACM8u4JPNH82XpM/PP2EwMlxQzxm4B4OecvzLT/TjnOzjnGznnG2tqaooUHSmWFp8DsZQy7nOxlIJmn0OniObODGOTc457Hn0dTouIN6+pxUd/sA/DkaTeYZECMsO4JAtD82XpM/PP2EwMlxQDuBbArYyxswAeBXA9Y+wRfUMixbZtSytSCkc0mQbn2seUwrFtS6veoZWEl88Mo2MwjA9cuRTXXFSNK5dX4qGnT+gdFiFkAWi+LH30My4OwyXFnPPPc86bOefLANwB4BnO+Qd1DosU2da2Wtx36zrUum0IxFKoddtw363raKdtnnx/9xncuK4BosAAALdc3Iifv9qNcCKtc2SEkPmi+bL00c+4OAzbko2QrW219IIvgFA8hRdPDeJbd1w29rlKpwVrGzz47YEe3LF5iY7REUIWgubL0kc/48Iz3EpxLs75TupRTEh+PXt8AG0NHris498TX9Vahd8c7NEpKkIIIURfhk6KCSH5t/O4HxuavJM+f2lLBV4/P4pALKVDVIQQQoi+KCkmpIxwzrH75OCUSbFNFrG63o0XTw3qEBkhhBCiL0qKCSkjXSMxKCpHg9c25dfXNXrx7HHqFUoIIaT8UFJMSBl55dwIVtW7wRib8usbmrx4gVaKCSGElCFKigkpI/vODqO12jnt15t8doTiKfQGYkWMihBCCNEfJcWElJHXO0exotY17dcFxrCmwYO9Z4aLGBUhhBCiP0qKCSkTKUXF6YEwllVNv1IMACtqXZQUE0IIKTuUFBNSJk4PhFHjtsImizPeb1WdG6+dHylSVIQQQogxUFJMSJk40h2cdZUYAJZVOdExGEEsqRQhKkIIIcQYKCkmpEwc6wuiqcI+6/0skoAWnwOHewJFiIoQQggxBkqKCSkT7b0htPgcc7rv8monDnVRUkwIIaR8UFJMSJk40R9CS+XsK8WAVkLxeudoYQMihBBCDISSYkLKQCCWQiieRpXLOqf7t9Y4cbBrtLBBEUIIIQYi6R1AqdjZ7sf2XR3oHImixefAti2t2NpWq3dYhADQOk80++wQpjnJbqImnx29gTiiyTQcFpomSP7RnElKEY1rc6OV4jzY2e7HvY8fgT8UR4Vdhj8Ux72PH8HOdr/eoRECADjtD6NxDpvssiRBwJJKB471hgoYFSlXNGeSUkTj2vwoKc6D7bs6IIsMDosExrSPssiwfVeH3qERAgA45Q+jzjO30omsJZUOHO0NFigiUs5oziSliMa1+RkyKWaM2RhjexljBxhjRxhj/0/vmGbSORKFfcKBCHZZRNdIVKeICBnv1EAYjd65rxQDWlJMHShIIdCcSUoRjWvzM2RSDCAB4HrO+SUALgVwI2PsKn1Dml6Lz4FYavxBB7GUguY5tr8ipNBO+8NomEf5BAAsqXLgKPUqJgVAcyYpRTSuzc+QSTHXhDN/lDM3rmNIM9q2pRUphSOaTINz7WNK4di2pVXv0AiBonL0jMYXVD5xeiACRTXsS4+YFM2ZpBTRuDY/QybFAMAYExljrwPwA/gT5/zlCV+/izG2nzG2f2BgQJcYs7a21eK+W9eh1m1DIJZCrduG+25dRztOy5SRxiYA9IzG4LXLsEri7HfO4bBI8NolnBuKFCgyUkxGGpc0Z5IsI43LxaJxbX6Mc2OvAjHGKgD8EsD/5Zwfnuo+Gzdu5Pv37y9qXKRsza2nWYYRxubuk4N44Ml2fOHmNfP+u9966gQ+fPUyvP3ihgJERvLIdOOSlI05j00al6SIphyXhl0pzuKcjwLYCeBGfSMhxJzODEXmXTqR1VhhR3sfdaAghBBS+gyZFDPGajIrxGCM2QHcAKBd16AIMamzgxHUuG0L+rstPju1ZSOEEFIWDJkUA2gA8Cxj7CCAfdBqin+rc0yEmNLZRawUt1Q6cKKPDvAghBBS+gx5fivn/CCAy/SOg5BScH4oije31S3o7zZ47egPJei4Z0IIISXPqCvFhJA84JyjezS24JViUWBoqrDjlD88+50JIYQQE6OkmJASNhxJQhLYolZ5W3x2HKcSCkIIISWOkmJCStj54SjqPAvbZJfVUGFHOyXFhBBCShwlxYSUsM6RGGrcCyudyGrxOagtGyGEkJJHSTEhJaxrJIoqp2VRj9His+NkP9UUE0IIKW2UFBNSws4NRlG9yJXiarcVoXgKgVgqT1ERQgghxkNJMSElrHMkitpFJsUCY2j2OXDKT3XFhBBCShclxYSUsK6RGGpci9toBwDNPjuO91EJBSGEkNJFSTEhJUpVOfoCcVS7F1dTDACNFXYcp812hBBCShglxYSUqMFwAg6LCKskLvqxmn3Ulo0QQkhpo6SYkBLVNRpD7QJPspuoxefASTrVjhBS5mJJBcm0qncYpEAoKSakRHWPxFDtyk9SXOm0IJFSMBJJ5uXxCCHEjFTO0R+MQ1G53qGQAlj42a9kVjvb/di+qwOdI1G0+BzYtqUVW9tq9Q7LEOjfpvC6R2OoXGSP4izGGFoqHTjRH8KVrVV5eUxCCmm2OYbmIDJfDz11At99vgORpAKHRcRfXbcc97xltd5hkTyileIC2dnux72PH4E/FEeFXYY/FMe9jx/Bzna/3qHpjv5tiuP8UBRVzvysFANaXfEJKqEgJjDbHENzEJmvh546gQefOYVYSoEoAPGUggefOYWHnjqhd2gkjygpLpDtuzogiwwOiwTGtI+yyLB9V4feoemO/m2Ko2s0hmpXflaKAaDBa0d7L3WgIMY32xxDcxCZr+/tPgOBAZIgQGACREGAwIDvPk9jppRQUlwgnSNR2OXxu/7tsoiukahOERkH/dsUR89obNGn2eXSehVTBwpifLPNMTQHkfmKJBUIbPznGNM+H0sq+gRF8s6QSTFjrIUx9ixj7Bhj7Ahj7B69Y5qvFp8DsdT4F0ospaDZ59ApIuOgf5vi6AvE87bRDgBaKrUOFJzTBhNibLPNMTQHkflyWkRM3FvHufZmyh+KI6VQR4pSYMikGEAawKc452sAXAXg44yxtTrHNC/btrQipXBEk2lwrn1MKRzbtrTqHZru6N+m8ILxFBSVw2lZfI/irAq7DM45BsPUgYIY22xzDM1BZL7+8rrlUDmQVlWoXIWiqlA58L4rmqFkDkpSqSOF6RkyKeac93LOX838fwjAMQBN+kY1P1vbanHfretQ67YhEEuh1m3Dfbeuo93NoH+bYugZjaHWbQVjbPY7zxFjDEsyHSgIMbLZ5hiag8h83X3DKtxz/QrYZRGKClglER+5aik+dM0yAEBKUeEPJfQNkiwaM/qlUMbYMgC7AKznnAdzPn8XgLsAYMmSJVecO3dOnwBJuZk1yzTC2HymvR/ffuY0/u5t+W0X9IMXz+DK5VX46HXL8/q4ZNFMMS5JWZpxbJptXEYSafQH49N+3WuXUZXHsjVSMFOOS0OuFGcxxlwAfg7gb3MTYgDgnO/gnG/knG+sqanRJ0BCpmCEsdk9GkeVS8774zZV2HGMOlCYkhHGJSETldq4DMRSCMZTeodBFsiwSTFjTIaWEP8P5/wXesdDiJl0j0Thc+R/taLF56AOFIQYVDSZRjxFnRD0NhRO0s/BpAyZFDOtEPL7AI5xzr+pdzyEmE3XSH57FGc1VzpwaiBMG0oIMZiRSBJ9ATp+2Ah45iho6khhPoZMigFcC+BDAK5njL2eud2sd1CEmEXPaKwgdW0uqwSHRUT3aCzvj00IWZiBUAIjUeoKU2jxlILP/+IQTg/MfrKnomqJMS0gmIukdwBT4Zzvxhw2jhBCptYbiKPKmf+VYgBYUulEe18ILZXU05UQPaUzHQ/oUn1xfOEXh/D4gR48d2IA37j9YrTWuGa8fzKtYiCcQJ3HVqQIyWIZdaWYELJAisoxGE6gskBJcbPPhvY+2mxHiJ7iKQXdozFKiIvorje2wueQEYil8KmfHsSZwcisfyeSSGM4Qqv4ZkFJMSElZiCUgNsmQxYL8/Ju9jlwpIeSYkL0MhpNopfqh4uurd6D739kEzw2SUuMHzswp8R4NJpEiDpSmAIlxYSUmO7RGGoK2CdzSSV1oCBED4rK0RuIYTiSpOPWdbK63o1vvO8SeGwSRmMpfPqnB3B2aPbEeJA6UpgCJcXE0NK0e3feegMxVBag80RWU4WdLtsSUmTxlILukRhiSXrd6e2iGhe+cbuWGI9EtRXj2RJjzjn8wQT9TjM4SoqJYaUVFb2B6U8OIlPrGY2h0lG4pFgSBTRV2HGyf/Yd2ISQxRuNJtEzGkNapYTKKC6qnZwYn5slMU6rKvpDCVrlNzBKiokhZRNi6vM4f90jsYJtsstaUumgk+0IKTBF5egLxGmjlkFdVOvC13MS40/OITFOpBQMhunnaVSUFBPDoYR4cbpHY6gqYPkEADT77DjSEyjocxBSzhJpBT2jMUSTab1DITNYUevC1957Mdw5ifH5oeiMfycUTyEQo413RmTIPsWlbGe7H9t3daBzJIoWnwPbtrRia1ut3mEV1Hy+Z0qIF697NIY3rirsmFpS6cAfj/YX9DkImUmh51I95+pwIo3BUAIqXWY3rP9+8Swee6ULsZQCuyzi+tU1ePbEoJYY//QAvvm+S7Bkhl7uw5EkLKIAu0UsYtRkNrRSXEQ72/249/Ej8IfiqLDL8IfiuPfxI9jZ7tc7tIKZz/esqBx9dDTmovUF4gVfKV5W5cTxvhDVxhFdFHou1XOuHokk4Q/GKSE2sP9+8Sx+uOccEmkFoqCt6j9xuA9vWlUNl1XCcCSJTz12AOeHp18x5pzDH6Lfd0ZDSXERbd/VAVlkcFgkMKZ9lEWG7bs69A6tYOb6PWsrxDEk0zRBLEY8pSCcSMNrlwv6PB67DJssoGuEjnsmxVfouVSPuTqbJNFxzcb32CtdEBggCgIEJmQ+As8cH8DXb78YLquEoUxi3DlDYkxHQRvPgpJixtjlM93yHWSp6ByJwi6Pv1Ril0V0jcxcf2Rmc/meU4qKntE4JcR5oK0SWyGwwp+SvqzKSXXFRBeFnkuLPVcn0yq6R2MIx6l+2AxiKQUTp1jGtM+vqnPja++9GE6riKFIEp/86YEZx032KGhiDAtdKf5G5vYdAC8D2AHgu5n/fyg/oZWeFp8DsQm9XWMpBc2+6euOzG627zmtqOgdjVOroTzpGY2hpsClE1lLqxw43E0dKEjxFXouLeZcHYyn0D1KV8nMxC6LmFjdwjnG3kitrs9JjMNJfOKxmRNjOgraOBaUFHPO38Q5fxOAcwAu55xv5JxfAeAyAKfyGWAp2balFSmFI5pMg3PtY0rh2LalVe/QCmam71nN1BBTQpw/PYE4Kp2FO80u19IqJw50jRbluQjJVei5tBhzNeccA6EEBqlvrem874pmqBxQVBUqVzMftc9ntdV78C/vuZAYf/KxA+ieodyMjoI2hsXWFLdxzg9l/8A5Pwzg0kU+Zsna2laL+25dh1q3DYFYCrVuG+67dV1Jd5+Y7nt+4+oa9IeoZCLfekdj8DkKW0+ctbzaiaM9tFJMiq/Qc2mhHz+tqOgJxCkJMiFZFPAX17XiI1cthVUSoaiAVRLxkauW4kPXLBt33zUNmcTYImJwDonxYDhJJxbqbLEt2Y4xxr4H4BEAHMAHARxbdFQlbGtbbUknwVOZ+D1rG0oS9OIvgM6RWNFWiqucFqQzhwvUe21FeU5Csgo9lxbq8WNJBf5QHAptrjIliySgscKGv3zjRZOS4KmsafDggfdcjM/+/CAGwgl88rED+Ob7L0FThX3SfbObLRu8dlgk6oOgh8X+q/8FgCMA7gHwtwCOZj5HyJSybdciCdpQUgjdI1FUF6mmmDGGi2pcONRNm+0ImYvRaBK9gRglxCYniQIavTa4bXO7Kre2UUuMHRYRA+EEPvXYAfSMTr1inO1IQWNEH4tKijnncc75v3LO35W5/SvnPL7YoBhjDzPG/Iyxw4t9LGIcibSCrpEorRAXUG8gjmpXcVaKAWBZlQMHO0eL9nyEmJGaSXRoM1XpYIyhxm1FlcsKNoduP1pivAEOiwh/SFsxni4xTikq+oNxqjXXwaKSYsbYtYyxPzHGTjDGOrK3PMT1AwA35uFxio76DU4tmVbRF6B3v4XEubYKX+iDO3Itr3biddpsR8i0UorWbo2ujpUmr11Gg9cGUZg9MV7X6MVX370BdvlCYtwbmDoxjqcUatWmg8WWT3wfwDcBXAdgU85tUTjnuwAML/Zx9DAUSdKZ5hOkM+96KSEurGAsDQbAYSne6e2tNS4c6grQigYhU4gm0+gZjdGpZSXOJotoqphbHfD6Ji8eeM/4xLgvMPUF9nA8jUCU8oliWmxSHOCc/55z7uecD2VveYlsFoyxuxhj+xlj+wcGBorxlHPCwTEUTsBPSSAA7d1uz2h5HWWp19jsCcRQ7S5e6QQAVDotkESGzmE62c7ojDpnlqpANEVXx+agVMalJApoqrDDZZ19UWJ904UV4/5gAp947PVpE+OhSIKuMhTRYpeUnmWMfQ3ALwCMrfNzzl9d5OPOinO+A9qhIdi4caPhZp1wIo14SkWN2wq7RZz2fjvb/di+qwOdI1G0+BzYtqXV9N0pst/T+eEIatw23LGxBZtbK/UOq2j0GpvdIzHUFLGeOGtFrQuvd41iSVXpHkJTCow+Z05k9Llxuvg45xgIJ+h0ujky27icCWMMtR4bfvD7Y/jhS+cQSymwyyLed0XzpE4VG5q1xPizvziI/uCFrhT1nsmdfAZCCYgCg02ePpcg+bHYleIrAWwE8BVcOOXu64sNqlSkVRW9gdi0myt2tvtx7+NH4A/FUWGX4Q/Fce/jR7Cz3V/kSPMn+z31BWJwWEQMhRN48JmT2NthymoYU+kJxFDpLF49cdbyaidePUc/X5I/Rp8bp4vv6aP96AnEKSEuYw89dQLbnz+DRFqBJGgbzH+45xz++8Wzk+67odmLf373BthkAX3BOD75kwPoC05eMVY5hz+YKKsrrnpZcFLMGGsD8I8A3pE94S5zyt3X8hZdiRiNJtEXiCM9YUBv39UBWWRwWCQwpn2URYbtu/KxV1Ef//ncaTDGIYsCGBjssghJYHh0X6feoZW8rpEYfDokxStr3dh/dqToz0tKl9HnxqniEwXg28+eQiJF3XXK2fd2n4HAAFkUITABoiBAYMBjr3RNef9LmivGJcafeuwA+qdIjNOqtlmdNvMX1oKSYsbY3QB+DeD/AjjMGLst58v/tNigGGM/BvASgNWMsS7G2McW+5h6iybT6BqJjTvBqHMkOnZWepZdFmc8I93IEmkFZ4cisIjjh5X2Yqea00LrGokWtR1b1vJqJ04NhBGnZIDkidHnxonxpVUVksCm7SRAykckqSDbiIIxBgZAYEBshvnxkuYK/PO7NsAmCegNxPHJaRLjlKKiP0St2gppoSvFfwXgCs75OwFsBfAlxtg9ma/N3pdkFpzzOznnDZxzmXPezDn//mIf0wjUzFn32VXjFp9j0gslllLQ7DNfbWY4kUbPaBz1HjviqfEr4vGUinrP5NN7SH71jMSLdnBHLpssotnnwGE6xIPkidHnxmx8nHOkFBWKwmmeIwAAp0VE7mIuYwwcDM4Z9hYBwCUtmRXjnMTYP0ViHEtSq7ZCWmhSLHLOwwDAOT8LLTG+iTH2TeQhKS512VXjD121BCmFI5pMg3PtY0rh2LalVe8Q52UkkoQ/02j8jk0tSKtc+4UB7WNa1T5PCqsnEENVkY54nmhlrQv7zlJdMcmPbVtaDT03btvSimRaRTCegqKqNM+RMX953XKoXLt6oHI18xH4qze0on6WfsaXtFTgK+/eAGs2Mf7pAQyEJifA4XgaI3QQTEEsNCnuY4xdmv1DJkG+BUA1gA15iKvkqZyjrcGDT96wEtUuKwKxFGrdNtx36zpD7bCeSfaUppHohRfn5tZK3HP9SlQ5rQjF06hyWnHP9SvLqvuEHpJpFcORpC4b7QCtrnjvGUqKSX5sbavFfbeuQ63bZsi58bKlPnz8TStQ6aB5jox39w2rcM/1K2CXRaRVreznnutX4O4bVsFhkdA4Sz/jS1sq8JV3rYdVEtAzGscnHnt9ysR4JJocV45J8mOhLdk+DGDc9lrOeRrAhxlj2xcdVRm5uKUCly7xwee0wGuf2znqRpBMawdyTLUbdnNrJf1yKLK+QByVTsucTlUqhNX1bvzwpbNQVQ5BpxhIadnaVmuYJDgrmVYxFEkgllSweXklNi+neY5MdvcNq3D3Daum/JosCmj02jEYTiA8Tf/hy5b48E/vWo8v/vIweka1Uopvvu8S1EzoQz8YTkIWBWrVlkcLWinmnHdxzvum+doLiwup/KhcO/CjN2D8k48UVYu1m05pMpSu0eikCbOYKp0WOC0iTvrDusVAzMkMm4YUlWMwnEDXSBSxJG0oJYsjCFo/45mu7F2+xId/eud6WCQB3aMxfGqKUgrOtau1yTT9Ls6X4p0HS2b1XPsAHt3fif5gHEsrHfjrN15kqJWSSCKNoXASaXXqF+DejmE8uq8TvcEYGjx23LGpvA7t0FP3SEyXzhO52hrcePnMEFbXu3WNg5hL10gMTqsEj03C7pODhjuwIxRPYTiSnPFkOpr7yHw89NQJfG/3GUSSChyygPde0YwPXb1s0v0uX+rDV965Hl/41WF0jWiJ8Tffd8m4uV7JlDE2Vth1u1JYShZ7eAfJk70dw3jwmZMYCifgsoroHo3hC786hCcP9eodGhRV65rRH4zPmBA/+MxJDEUS8NgkDEXo0I5i6h7R5+COXKvrPHjh1KCuMRDzUVSO0WgSP3ulC1/41SH0BWOGOLBD2xAdxUAoMWtCbIS5L5xIY9/ZYTz09El0DNAVG6N66KkTePCZU4iltMM94mkVP3zpHP5nz7kp73/5Uh/+8bZ1sEgCukZi+ORjBzA0oftESlHhD019TDSZH0qKDeLRfZ2QBO2wi+yhFyJj+M/nOtA9GtOlB2xaUTEUTqBzODprQf9U8dOhHcVzfiSKKh3aseVa1+jByx3D1FyeLMijezshMgZZEJBSOGyyqMuBHSlFRc9oDH2BuV2W1mvu6w/G8fSxfjz41En81Y/247Zvv4DP/vwQtu/qwG56c2pY2cM9JEGAwARIggBRYHhsfyec1qkv3m9cVon7b1sHWWTTJsaxpDLt6blk7qh8wiB6gzF4bON/HNlDLxIpBT2jMbhtMqqcloJvZFJUntnZmp5zvd9M8ZPC6xqJYXWdvmULVS4rHFYRJ/whtNV7dI2FmE/uHMI5RyrNIQkM54cjRXl+zjmCsTRGokmo86hzLsbcp6gcZwYjONQdwOHuAA53B6fsVSswbdOrw0K/2o0qktRWiHMJDIimVNR5bBiNJqdMbjctq8Q/vnM9/v5Xh9GZSYy/+b5LUJVTSjEaTcIqCdMm12R29C9nEA0eO4YiiXGnJE1sBh+KpxBJpOG0SnBaxbxPfKrKEYynEIilZrxcOJW5xE8Kp3skputGu6x1DR7sPjlISTGZt6nmkGhSQY3Lhq6RKNw2uWAdekLxFIbC80uGswox98WSCo71BccS4KO9QUSn2OBnl0WsbXBjXZMX6xs9WNvowfJqFyVFBua0iIilLpx6BwAqx9jhHhUOCyySAH8wMWk8TkyMP/XTg/jm+y4ZVzo3EEpAFoUZ276R6dErxyDu2NSCB585iVhKgU0WEE+pUzaDVzlHKJ5CKJ6CLAqocVthlQQwtvDV41hSQSiRQjShLOiXwnziJ/mnqBz+UFz3jXYAsK7Ri+dODOAv32CMQxaIecw0hyTTWilXIJqCzSLAZZW0coVFzHucc0SSCkYiyUV10snH3DcUTuBQdxCHewI40h3ESX8IU61LVLks2NDoxfomD9Y3eXFRjYs2V5nMX163HA8+cwppVYXAtIRY5drns7R+xsKUbU83LavE/betx5d+fRjnh6P41GMH8I2cxFjNdKRoqrBTe8wFoKTYIDa3VuIerMSj+zrRF4yhfg47mLO1bwBglUW4LBLsFnFO7xA55wgl0ghEU3lprbaQ+El+9Afj8NhlyKL+KwPrGr3Y8XwH4imFemeSeZnLHJJWVYTjKsLxNBhjkAQGl1WC0ypBFtmckmRF5Qhn5r7pNg7nO+5cKuc4NxTVVoF7tNXg3sDkTVIMwPJqJ9Y1ebChyYv1jV7UeayLeiNA9JftX5ztPuG0iPjL65ZP6mtskQQ0VdgxEE4gMqGf8ebllbjvtnW499dHcG6KxFjbeJdAvddWnG+qhFBSbCDZQy+y7X3+9ekTaNg3t+QykVKQSClABBAYg0USIIlMK+JnDBwcSUVFWuFQVI60yvPSH5RaEemvaySGWrcxJj+XTcKSSgf2nR3GG1bW6B0OMYmJ88jfvnnVrPMI5xwpRdv/MBJNQmAMDqsIt1WGPXMpOplWEUsqUDiHwLSyhmhy6gMTFiMba/Z7yG6y29xaiURKQXt/CEeyK8E9QYTik2OwSALW1LuxvklbCV7X4IXLRr+iS9FMh3sA41u2OS0i/uzKJbh94/grD1cur8L/u3Ud/uHxTGKcadfmc2iJcTSZ1vWUU7OiV5zBZNv7SAIb197nHsz9CFGVc61bRYFPgMxHrGTxOoejqNG580SuDc1ePNvup6SYzMnLHUN5mUdUzhGOp8dWkVnmc8WQOxc6LCI6R6K4/4mjqHJZ0TMaQ3qKWgifQ8a6bClEoxcr61yGuNpD9JVt2aZ1qABiKQU7nj8DSWB478aWcft9rmqtwpffkUmMhy6sGGcT49FoEhZJKzcic0OvQIMxU2szM8Vays4PR8btQNbbpc0VePqYPr1lifn8uADzCOe8KAkx5xydw1F8Z+cpBGMp9AXjODMYxVAkiUhSwfnh6FhCvKTSgZvX1+Pv3rYaP/roJvzsr6/Gfbetw/s2tmBto4cSYgJg6pZtAgN++NI5NFbYYZ1Qlnb1RVX48q1rIQkMZ4ei+PRPD2I0eqF7xWAogUSaTmGcK3r7YDBmam2W71hTiorRaGrscuhIRPt/RQU+c+NqqlGdxtmhKOo8xiifAIBl1U6EE2l0DITRWuPSOxxicH2BGNwmmfNSioqT/WGtNVpmU9xobPIlOQbAKmula5+7qQ3rG73wOgrTOYOUlulatkWSCmRRQKPXhoFwAuGcEpxrLqrGl29diy8/fhRnBiP41E8P4hu3X4wKh0XbeBdIoLHCBoneeM3KsEkxY+xGAA8CEAF8j3P+VZ1DKgoztTabS6zJtDopyZ3u/4NT1NllffS6ZWj2OQr6/ZhV53AUG5q8eocxRmAMly2pwJ+O9mPbGykpJjOr99oxFDbmnBeKp3AksxnucE8Q7X2hKQ/0EAUGWWTaZmdZhFUWkEirqHJace2Kah0iJ2Y1W8s2xhhq3TZYJe348ezeoGsuqsY/vGMt/t9vtMT40z89iG/cfgm8DhlpVUVfpiMFbdScmSGTYsaYCOA7AN4CoAvAPsbY45zzo/pGVni57X0UVcVgJIm0wiGLAvZ2DBuiVjeRUvBs+wD8wRh6gwkIDHBYRHAAaYVDFBg+8vBeDEeTiCQWdtlGEhh8DgsqnRY0+ewoUmmgKRlpo13WFUt9+P3hPmx740V6h0IM7s5NLfjqk+3oD8ahqNr84bRK+PjWFUWNg3OO3kAch3uCONIdwKHuAM4ORae8b2OFDetzWqP1jcbxb8+eQiqtYDCSQDKtQhAYbmirLer3QMxvppZtEzfgfeTqpePqjK9doSXGX/7NUXQMRvCpnx3AN96rJcbJtIqBUAK1BrqqaESGTIoBbAZwinPeAQCMsUcB3Aag5JPibHufHbtOo3s0AUlgqPdYkVLUgm5ii6W0IyJHMyu3w9EkRiJJjGTLGXL+f2ITeYUDoZzkd7pfJBZJgM8hw+ewaDfnhf+vdI7/vMsqjb2jpUvw00ukFYxEjbfDeF2jF9959jT8wThNwmRuGMCY9rEYFJXjlD+Mwz1aAnykO4ihKU4SEwWGFbUubMhsiFvf5J30eltW5cTJ/hAe2XseisphERncNglPHu3H6nqPIRYziDlM17INwKQNeP/+XAcExnB7ppc3kEmMb1mL//fbo+gYiODTPzuAr99+Cbx2GeFEGhJ1pJiRUZPiJgC5uyy6AFyZewfG2F0A7gKAJUuWFC+yItjcWolH93WiqYKPu6QYSyl4dF/nnCZYzjmiSS1hGo1ql1lySxWGJ5QwxFML69fJoK3qMgbYZBFbVtVcSHydFvgcMiqdWrLrsCyu2b5ZFHNsdg5rJ9kZrYG/LAq4fEkF/nCkDx+6epne4RAYd8788b5OuKwSanI2i85nrpurSCKNY71BHO4O4lBPAMd6g1POe06LiLWNWgK8odmL1fXucfPwdF7rDKDeY1vwnF2ujDou9TRVy7aLv/yHsQ14gFZnnFZV/NeLZ3HPDavG9TO+buWFxPj0QASf/umFxJg6UszMqP8qU/2GH3cBnXO+A8AOANi4cWPJXVyfahObVWLoCURxfjg6PsGNTEh8o9qq7lS1b3Nhl0X4nDIqHRZUZFZuc///wadOwmOTIAvC2Ik5HByheBqffMv0vRfLRTHH5vnhCOoNuhK7aXklfvV6DyXFBmHUObNQG+0GQgltQ1zmqOSOwfCUp8TVuq1ab+BG7ZCMZdXOBb3JNNMmaSMx6rg0mpk24AkCQ53Hlrmiq13tuG5lNb50yxrc/9tjOD0Qwd/97CC+/t6L4bHLGAhpV6Fp8/pkRk2KuwDkdqpuBtCjUywFxbmWTE4sV+Ac6BnVTjlKqxcO3ACAP/+vffN+HqdFzKzcXihbqJxQwpD9/9leKL96tQdDkcS4IySNsjGm3JwbiqLGbZx2bLkuaa7Ad3d1oDcQQ4OXxgaZWj422ikqx9mhyFgCfKg7AH8oMel+AgNaq105p8R58lbeY6ZN0sR8ZtuABwA+pwUWScBAKAGVc2xZWYMvvR2477dHccofxqdzEuP+YBwNXvucTsAtJ0ZNivcBWMkYWw6gG8AdAD6gb0hzp3KOYCylJbiZd27DOf8/MuH/lamWL+bAbZMyCa08timtIqdcocIhw+fUkt98Dfy9HcMIxpLoHo1BEhiqXRaIgoC0ynHHppbZH4Dk1bmhCKoN1KM4lywK2LSsEr9+vQd/TRvuyDTu3NSCbz19EiPRBELxNJKZzbozbVKLpRS09wbHjkk+2hNEJDl5U69NFrCmwYMNjV6sa/JgbYMHzgJdNr6sxYtH9p6HqnJYJAFOiwhZEmleJHkx0wY8YPwpeA5ZwPs2tuDPrlqKLatq8KVb1uL+TGL8dz87iK9lEuO+QJxatU1gyKSYc55mjP0NgD9Aa8n2MOf8iM5hzer//M8reLljGCPR5JSX6ebCa5fHEltV5TgzGEEonobAAK9dgixqXR7uvn4Frilyq5/cU5vqPVYMRpLoCyawrMqJj29tpbo5HZwZjOKKpT69w5jWNSuq8eOXz2PbltayqCcn83dlaxVu7A2OJZTWTEKZu0ltOJLE4UxHiMM9QZzyh6dcTKhyWnJWgb1YUesqSr393o5hPHm0HxV2CaF4Gom0irTK8cHNjTQvkryYbgPe3TesmnQKXjyt1RoLDLjzyqV446oa8LevxT8+cRQnM4nx12+/GG6bjL5gHI1e+7grv+XMkEkxAHDOfwfgd3rHMR+BWGrS7mUGaCu22RXd7Gru2Ea0nM/b5Unv2D75kwOTLsnFUgp+9kp30ZPi3BPsABEuq4xYSoHHJtPEr5OzQxG8fUOD3mFMq63ejXAijUPdAVzcXKF3OMSgcjepcc6RVDiC8RQe+EM77BZxrJRsomVVDq0euMmLDU0e1Htsurz5ys6NbqsVPod25SaWUvBaZwAfKno0pFRNtQEPGH8KHoBMiYWKR/d14q43rkAonsLW1TUAxifGX3uvlhj3h+K6vXaMxrBJsRl97LrleOvausxGNS3h9drlRa1U5G7eCCfSGIkmkVK0RtzF7Fu8t2MYR3oDY5cGK50WOC0SbSTRUVpR0TsaN9RpdhMJjGHLqmr8eO95SorJlBJpBeeGIxCYtjkuPmGD8EhUOzHOIglYXefWWqM1ebGu0QO3zRinxJ0dCiORVpFSVMiiAJ/DAqdVpLmRFMV0m/DCCQVv/sZOhBNp2GUR77uiGX//9jX4xyeO4UR/GJ/52SF87b0XAwD1MM6gpDiPrm+rgz8UH3f84mJlN28oKoc/FAcDQ7aVZyH7FufKlk0wlm0Dw+EPJlDr0ZIe2kiij57ROLwO2fAbJd64qhaf+8VB/P3b1xasnpOYz7mhCD752AEc7BpFSpm63owBuGl9PW7e0ICVdS7IBqx93NsxjEhSAeccAmNIK9pcXZGW0exz6h0eKQNTbcJLpTk4tCsWssiQSCv44Z5z+MhVS/HFm9fgn353DMf7Q2MrxgCohzEA480wZJw7NrUgrXIMRhJan7rMf2rcVkgCw6P7Omd+gDzIXhqsdlq1vniZ/wyEErTBTkdnhiJo8Br/nX2l04K1DR78+vWSbCBDFqjKZcVr50fGJcQMgMgAiWkfq5wyekbjWNvoMWRCDGjzY4VdAgMDB8AyYQbiaZobSVH85XXLoXKtb7HKVe0jsucICBCYAFkUITDgp6924U1ttfj8TWsgMGiJ8c8PIpxIYzSaRDCe0vvb0ZUxZxkyZnNrJe65fiV4ZqcpOAdjQH8wjsFwAueGwgWP4dxwBAOheGalWjt1ikO73XN94VeqydTODkYMXTqR6/q2Wjz8whlwOq+bZLisEr5w8xrcf9s6/Pz/uxoVdq0ci0M7JVPlWsnYueGI3qHO6OxQGOGEAoVzpBWOVJpDEhhcVonmRlIUd9+wCvdcvwJ2WURaxdgeJIs0vnRTFBjCCQW3fns3/vn3xyAL2pXn430hfOZnWmI8GEogmszf1W6zoaTYBDa3VmJdgxc+h4xMXgyBMaQUFeGkgr0dwwV77r0dwwgn0kirXNudyhg4B3wOGesavDTp6+iUP2zYgzsm2tDkRSKt4MXTQ3qHQgzkL9/QijesrIHPYcGyKhdcVhEiY5BEBlliSKsc4UR6UXOcRRLgtmmbnavdVtR7bWj2OdBYYUetx4YqpxVOqwRhAZuMsqUTKUWFJDBIAoMoaAn/0koqnSDFc/cNq3Dwy2/D6a/cjINffhs8NmlSF6xUWvtEIq1CEgAls0jBALT3hfDZzIpxfzCBeGpyi8NyQEmxSdyxqQWBTK0yE7RVWgaGCrs05xIKmyzCZZPgtEqwzvEkm0f3dcKb2ejHVe3Fw8ExGqNLg3o75Q+bonwCABhjuGldA/5j52m9QyEGdcemFozG0uDg2jyT2W/ntc19jsuSRQHVbiuWVDrQ7HOgxm2Fz2mBxybDYZFgkQRtPrRK8Dpk1HlsWFrlQJPPjnqvDdXuuSXKVDpBjGq2kgpREDMfAYuorRgf6w3hcz8/iHA8hf5gfMGn4poZJcUmsbm1Ei6rBElgUFU+VkYxEk3hSG9gxpUUh0VCk8+urYy4bajz2NBUof3Zbpk+Oc52nBiJpsbKJlTOIYsCXBaRVol1dmYwgsYK82xyvG5lNY71BnGkJ6B3KMSANrdWagdeiNphQNpJnvMroZBFAQ1eO1oqHfDYJre4nAljDFZJhMMiwWO7kCg3Vtjhtctj7a6yxs2PDADnUFUqnSDGMJeSCsYYVBVI5NT1H+3VVoyDMS0xXujhYmZFSbGJLK10osZtQ63bNlZGkduJYqrEuNJpQb3XBqs0Ofm1ySIavFpyPPFo54kdJ7JlE3UeG6pdViytchXkeyRzE01qR4PXGPQ0u6nIooCbNtTjoadP6h0KMajFlFB47TKafTO/0Z8vxhhssogqlxVLqhyo99rgtsnYfzYzPyJz9YxrV+9q3TbUuG1UOkEMYbaSipSirR4DgCxirHvF0d4QPveLQwjEkugNxLSFuDJBSbGJTOxEoXKOlKq1SBuKJLBj14VL09kVkwrH7O1VbLKIxgo76jy2sZ7KO57vwFA4gVRaew7tRUEdJ4yiY0DrPGG2U4je3FaHvWeGcaw3qHcoxIAmllAoCkdaBVIKx/1PHJ0yMZZFAY0VdlS5rAU/fMBhkVDjtuLh3WcwHNHmwpSqzcUMwGCE5kdiXJNLKrRkV2SAwARYcq6sHOkJ4tZvv4D/fPYU+oLxstkkTUmxieR2okir2g5tkQGSwMBVjrPDUew7Mwyfw7KgFROnVUJThR0HOkdxdigCzjlkiUFk2nMpnDpOGMUpfxjNPvOUTmTZZBG3XNyIf3myXe9QiAFNLKHIznGyCMRTyqQrYnaLiKYprnQV0s52P04OhME5YBGFsfkxrWorxjQ/EqOaWFIBaEmgRRLBGBtLkrNUDjyytxP//sxJ9AcTxQ9YB5QUm0y2E4UsMcgCgyQKUMdWK4B/fOIoDnSOLnjFZPfJQfzD40egciClaptdJFGALGo7q6njhDGc6A+hwWu+pBgAblhThyM9Qew7W7iuKcS8llW5UO2ywipl5h1RgJqZ3/qDcW3F+MwwqpxWNHjtRb1asrPdj7sffQ2KCqRUDkXlsEgirKIAi8SwoYnmR2JsuSUVHpsEUbzw+slNiqWc19Ujeztx/defxT8/cbSoseqBkmITumNTC9KKVs6gKCoyXVYgMSCaUnDv40ews90/78fd2e7HvY8fQTTnyMiUqj0HOKfLggZyvD+EJhNtsstlkQTcvrEFX378SFnVqpG5yZaJJdIqAI60okLhWr2jKGgrxt959hReOz9S1Liy82MkmYYsaKvCKZUjrSjg0Mo87r5+JZp9Drjo5EZiAhPLKbJEhknlEtGUih3Pn8FXf1faiTElxSa0ubUSy6qc2uWOzLgVACgAVBXwh+L46u+PzftxH3iyHf7MblNF1V4YjAFpDggCw7JKB62CGMTJ/rBpk2IAuOaiKigqx2P7C38iIzGXbJmYXRbHVohFhrEVYw6gJxDD3Y++tqA3/wuRXSHuGY2NbaqziAIYtLIJgTGsqnVha1stLJKAWo+NkmNieFOVUzBo5RRTnbzOAfznrjNY/w9P4qGnThQz1KKhpNik7npDK6pcVoiZ41BVXOhGkUipaO8P46Zv7ZrzL42HnjqBY30hxNPq2Gl1Cs8ctypou6rvuWFVAb8jMlexpIK+YBwNFeboUTwVgTF8+OpleODJdgxHknqHQwxmc2slvvT2taj12MAyc5CSqTEWmPaLKxhP42M/2ocb//W5giXHO9v9uOlbu/CxH+1HMJ4GwCEwhrQKqFyFRWIQBYZajw2fvbFt3N/NJsdLKh2odFoMe0w1KW+55RSfvGElBIEhrc7cnzicUPDNp05i1Rd/V3LJMb1KTerKi6rw+Rvb4LBo7+gYcpLjzH2O9YWw7ZFXZhy0O9v9uPFfn8M3n7rQJovlfEypgNMi4f7b1uO2y5pQ57FN6tdJiuukXyudMPvPYXm1E9esqMa9vz6sdyjEgDa3VuITN6yE0yJC5Qwq55AEbZd8StWuYomMoWMwgm2PvIIr7v8j7tyxZ9EJ8s52P+7csQcb//FP2PbIKzjlD2lXzaCtCmuHH7DMZWcOh0XEfbeuw9a22ikfTxIFVGQ2P1e5rGMdfggxmtyV4yxxhuGaVHjJJcd0bceERIGhzmPD8uomeOwyPvaj/RAZJu0cBbTjHL/51En89JUu3H/b+rGJe2e7H3//y4PoCkzeUZr7KKIAPHTHZWN/z2mVYJdFDEeTCMZSBfn+yMza+0JoMWHniancfkUzvvDLQ3jycB9uXF+vdzjEIESBwWuXcfvGFtS6bbj38SPoHo1CFNjYKVtSpnd6SuWQRW316rXzI/joD/fBKglwWESsqvNg25bWaRNWQJsLt+/qwEl/CNGkglhSgVUWoCoqOGNIqYAsaIcWJRUVac5hERmgMjRW2GdMiHMxpn1PHpuEaFJBKJ5GNJnO278ZIflw9w2rcPcNq/DQUyfw4DOnIDCtNeJMssnxN586CYvI8DdvWoG7TXpl2XBJMWPsdgBfBrAGwGbO+X59IzIWxhhq3baxFkRb22qxqtaFM4MRqDP0EewcieHPf7BvXs/FAayscU2a8AWBodplhV0WMRBKzPi8JP/ae4OmrifOZZVEbNtyEb7wy0O4tKUC9SY5tpoUBmMMHpsEn8My1lVia1st7gNw96OvIZpUAACyoK3AxlNK5jQ5IKGokEVtBTeeUpFUVOw/O4S9Z4fQ4LHBZZUwGEkimVbBOQdjDJxzJBUOh0VAOK4glVlYSKe1DcyyoPUfTnPAJjLInI2dtue0SHNOiCd+j06rBKdVQjylYDCcKMvjdImxZZPa7+0+g5Qy9zdv2QT5sX3nsfvzNxQqvIIx4vXXwwDeDWCX3oEYUZXLMqn/8GdvbEOtxwabnL8fpywyWCUBn7tpzbT3cVq146MtkhGHUek61B3AsurSOTFrVZ0bN6ypxcf/91WkFEoOypXDIqElU2Iwsc3a1rZaPHTHZWissMMqCZnjaTk4tH0PaVU79j57JC0HoKhaMssAdI/GcdIfxnA4iUgyjVBCQTiRRjihIKWoGI1eODAE0MrQssmwJGgr0irXnsMqC2iqcIy7grZQNllEs8+BOo8N1iL2WiZkLrL1xp+8YeW8y366Agncuf3FAkVWOIbLZjjnxzjnx/WOw4i0S2/ypM9vbavFfbeuw7JKR96eS2AMH9960ayTviwKaKqww2Uz3EWHksQ5x7HeEJZVlU5SDAC3XdoEzjnu/21pt/shkzEG+BzacfTSDJvRsvPc8mqnto9CACyC9gDZ5HjSRSuuJcccWhcLPuE+POejyrVYAO0+2WQYDFoiDkDhHMsqHQtaIZ5J9uCkGrfV9HsFSOnJ1hpbZyownsJLZ4rbNjEfTPvqY4zdxRjbzxjbPzAwoHc4Bee0SqhyWaf9+ta2Wjz5iTfikzesxGJPOl1T78b2D14x55qgbElHlbPwx6yaQSHHZudwDHZZhMc++c2RmQmM4f/bugLPtPvxoxfP6h1OSTLqnFnjtsLnnP04ekCb537/t1vw/Q9vxGUtPngcFgiMQRYw5dzDmJbwCpmPnF9IirPdetScj2MJaaYdpUVkEBiD2yrisiU+fP/Dm/DkJ96Y14Q4l9smj23IK5duFUYdl2S8u29YheP/pHWomG9ybCa6LO8xxp4CMNWumi9yzn89l8fgnO8AsAMANm7cWNJFrXaLiFr39Alxrmwi+9AzJzHfMjVZZPi/iyiQ9zpk2CwC/MFEWV8GL+TYPNQdwPKa0lolznJZJXz6ratx/2+Potptxc0bGvQOqaQYdc50WOb/a2hrW+24TcMPPNmOE/4wZJEhrXBkZx8BOUlxZtWXIZMQM+3NmKJyCAIyPZA5RCHT951zrKxx4XM3rSlYEjwVIbPJ0GuXEUsqCMVTiCSVSYcplAqjjksytdyNeN9+5iSSJfarXpekmHNuvuprnTgsEuo881uBvfuGVbi4uQJf/f0xnBoIz5ocywLDiloXPntj26Inf6skorHCjoFQgnZWF8DrnaNYVpW/MhmjqfPY8HdvW40v/vIQBMaoIwWZVTZBnqqLhCQJ8FlFjETTEBjXkuFspgwtMa5wSIgmVVhkBsYYLCLDyjl0rSgGu0WE3SIiragIxdMIxlNjddOE6OnuG1bh/755Jb7yxFF8d/fZKe9z9XJfcYPKAyoENTCXVUKNe2ElCRNXUnIT5GyfzXwlwhOJAkO914ZgPIXhcJK6U+TRa+dH8Ja1dXqHUVBLq5z49FtX4/O/OIhIIo33XNGsd0jEBHLnPOBCq7WukShW1trAOZ/UfcJICfBMJFGAz2lBhUPGcCSJALXDJAbAGMPnb16LP7+uFe/7jxfQndPi9erlPvx42zU6RrcwhkuKGWPvAvBvAGoAPMEYe51z/jadwyo6r12esYZ4Pib+sigWj02GTRLhD8Wp5VAepBUVR3uD2LblIr1DKbjWGhe+cPMaPPBkO7pGorj7zSupXp3Mi17zXiExxlDlssJplTAQKu8yNWIMgsBQ77Hh0W3X4NF957GnYxg//Ohm0x5xbrhKfs75LznnzZxzK+e8rtwSYsYYatzWvCXEerNI1J0iX9r7QqhyWcrm37LZ58CXb12HJw714q8feQWhOK2OEQJkW7nZ4S2xDbfEnLJXh9+3sQU/+ugm0ybEgAGT4nImCgwNXhvcU7RdM7Nsd4rqBZaCEM0r50awus6tdxhF5XNY8MWb10LlwM0PPo/XO0f1DokQQ8iuGjdW2Cf1riek2GRRQL3XBlk091ikpNggLJKAxgr72El1pchjk9FYYSubVkP59nLHEFbUuvQOo+gskoCPXrsc7768GX/xX3vxzT8ep3IcQjJssogGrx2NFfYFdfIgJF+skmj6w7zMHX2JcFolNHrtZZEsWiURTRV2OE18eUUPnHPsOTOMtQ0evUPRzVWtVfjHd27AC6cGccu/PY+DXaN6h0SIYdhkEfVeG82thCxC6WdhBlfhsKDOY5t0rGkpEwSGOg8d9jEfp/xhWESGGrdN71B0Vem04FNvXY0b1tThIw/vxZcfP4xwglr/EUIIWTxKinWS3VBXOceTnEqR1yGjwWujY03n4IVTg1jbWL6rxLkYY3jDyhp89T0X4+xQFG/+xk48ebivZA83IIQQUhyUjeigVDfULYRNFtHko40is3m63Y8NTRV6h2EoHpuMbVsuwl1vaMU/PXEUf/5f+3B+KKp3WIQQQkyKkuIik0WtRVkpb6ibL+1Ngh0VjvJdNZ9JPKXglXMj2NDk1TsUQ1rb6MVX3rUBjRU2vOPbu/HNPx5HLKnoHRYhhBCToaS4iGRR6zAhlcGGuoWodFpQ77VBLKP66rnYfXIQrTVO2kAzA0kUcOslTfjHd67HvnMjeNPXd+LxAz1UUkEIIWTOKDsrElkU0EAJ36wcFgmNFeXRiWOufneoF1csMd8Z8nqodllx9/Ur8VdbWvHgUyfwjn/bjd0nByk5JoQQMivKPIogmxDTCvHcZFfUqeemVjrx1LF+bFpWqXcoprK2wYP7bluPN7XV4rM/P4h3/8eLePpYP1SVkmNCCCFTo6yjwCghXpjssZGBaHkf7bvzuB/Lqpwlc+x3MQmM4ZqLqnHl8irs6RjCV353DF9+/Ag+cOUSvOuyZtR7y7u9HSGEkPEoKS4gSogXz+so7w4d//vyeVyzolrvMExNFBiuXVGNay6qwkl/GLtODODfd57GihoX3rquDtetqMHaRg+VNhFCSJmjpLhAKCEmi9U5HMXrXaP42HWteodSEhhjWFXnxqo6Nz5yzTIc6QngQFcAP97biaFIAmsbPLi4uQJrGzxYXe/GRTUuahVICCFlhJLiAqCEmOTDd5/vwNZVtaY/S96IZFHApS0+XNqibWAMxVM4PRDBuaEIfvlaN7pHY+gejaHaZcXKWhfWNXqwvsmLS1sq0Fhh1zl6QgghhUBJcZ5ZRRGVDgslxGRR+oNx/Oq1bnz1PRfrHUpZcNtkXNpSgUtbKsY+p6gc/mAcnSMxnB+OYN/ZYZzoD8NpEXHtimrcsLYOW1bW0GoyIYSUCEqK86zca2BJfnz9D8exdXUtfHSgiW5EgaGhwo6GCjs2L9e6f3DO0T0aw8GuAL7z7Cl86rEDuL6tFndsasFVrVUQqC6ZEEJMy3BJMWPsawDeASAJ4DSAv+Ccj+oaFCFFtPfMMJ5p9+Nf3kurxEbDGEOzz4FmnwM3b2hAIJbCi6cH8YVfHgLnwEeuWYrbN7bQEe6EEGJCRrzG/ycA6znnFwM4AeDzOsdDSNGMRJL425+8hj+/dhn1aTYBr13GTesb8JV3bcCfX7sMTx3z49qvPoP7fnMEXSNRvcMjhBAyD4b7rcs5/2POH/cAeK9esRBSTNFkGh/74T5sXFqJjUvpsA4zYYyhrd6DtnoPBsMJ/PFoH25+8Hlcc1E1PvaG5di41AfGqLSCEEKMzHBJ8QQfBfCTqb7AGLsLwF0AsGTJkmLGRMiMFjI2ewMx3PWjV1DjtuD9m1oKGR4psGqXFR/YvBTvvLQJu04O4G8ffR0Oi4g7Ny/BbZc26nYQC82ZxIhoXBIjYZwX/9hTxthTAOqn+NIXOee/ztzniwA2Ang3nyXIjRs38v379+c/UEImm9dy32xjM5pM439fPo/vPHsKb1tXj1svaaQVxRKjco6jPUE8f3IAr54fxfomL25cV4c3rKpBa7UzXz/vvI5LQvJozmOTxiUpoinHpS4rxZzzG2b6OmPsIwBuAfDm2RJiQszo3FAEX/vDcTx3YgBrGzz4/E1r0FLp0DssUgACY1jf5MX6Ji/iKQUHuwJ47sQAvrPzNNKKOnZgyEW1LrT47Kj32lDlssJpEekNEiGEFJEuK8UzYYzdCOCbAN7IOR+Y498ZAHCuoIHNTzWAQb2DmIGR4zNybABg45yvn+udpxub7ituraq84a5l6fBwUo2F0nN6MK5KYMLc7lsoFENeYxAdHll0+ubcqqLn+x8/nBo8l8j8Mfe1Msg5v3Guj5MZlxHo91rT83VOz11ccx6b8/hdbuTfE0aODaD4sqYcl0ZMik8BsAIYynxqD+f8r3UMad4YY/s55xv1jmM6Ro7PyLEB+sZnhH8biqF0YijXsUzPbX5G/l6MHBtA8c3GcBvtOOcr9I6BEEIIIYSUFyP2KSaEEEIIIaSoKCkujB16BzALI8dn5NgAfeMzwr8NxaAphRjKdSzTc5ufkb8XI8cGUHwzMlxNMSGEEEIIIcVGK8WEEEIIIaTsUVJMCCGEEELKHiXFhBBCCCGk7JVEUnzjjTdyAHSjWzFu80Jjk25Fus0LjUu6FfE2ZzQu6VbE25RKIikeHDTy4SyknNHYJEZE45IYEY1LoreSSIoJIYQQQghZDEqKCSGEEEJI2TPcMc+EZO1s92P7rg50jkTR4nNg25ZWbG2r1TssUuZoXBJC9EBzT+HRSjExpJ3tftz7+BH4Q3FU2GX4Q3Hc+/gR7Gz36x0aKWM0LgkheqC5pzgoKSaGtH1XB2SRwWGRwJj2URYZtu/q0Ds0UsZoXBJC9EBzT3FQ+QQxpM6RKCrs8rjP2WURXSNRnSIihMal3nadGMDvDvWixmXFnVcuQWOFXe+QCCkKmnuKg1aKiSG1+ByIpZRxn4ulFDT7HDpFRAiNSz19+5mT+LufHYBVEtAxGMHNDz6Pp4/16x0WIUVBc09xUFJMDGnbllakFI5oMg3OtY8phWPblla9QyNljMalPl48NYgfvngW//COdbhxfQM+eNVSfOqtq/Hpnx7As8epppKUPpp7ioOSYmJIW9tqcd+t61DrtiEQS6HWbcN9t66jnbZEVzQui09VOb7068P48NXL4HNYxj6/otaFv71hFT7x6Os45Q/rGCEhhUdzT3FQTTExrK1ttfSCJ4ZD47K4/ni0D6LAcMVS36Svrapz4/aNzfjrR17Bb//vdbDJYt6fX1U5njzShz8d7YNdlvChq5diTYMn789DyGxo7ik8WikmhBBiWD988RzeurYejLEpv/6m1bWo81jxld8dy/tzD4UTuPO7e/CvfzqBSqcVKue4Y8cePHm4L+/PRQjRHyXFhBBCDKlrJIojvQFsWlY57X0YY/jza5bj94f7sOvEQN6euz8Yx3v+40XUe2348jvW4YY1dbjt0iZ85m2r8bmfH8SZwUjenosQYgyGTYoZYyJj7DXG2G/1joUQQkjx/f5QLzYtrYRFmvlXlcsq4a/e0Iq/++kBjEaTi37eQDSFD3x3D65cXoU7Ni2BIFxYpW6tceHtFzfgvt8cWfTzEEKMxbBJMYB7AOT/ehghhBBT+N2hvilriaeyocmLy5f68PlfHALnfMHPmUgr+NgP96GtwYN3XtY05X3etq4eh3uCONQVWPDzEEKMx5BJMWOsGcDbAXxP71gIIYQUXyCaQntfCOsavXP+O3dsWoKjvUH8ZF/ngp6Tc45PP3YAksjwgc1Lpr2fLAp469o6fG83nSZGSCkxZFIM4FsAPgNAne4OjLG7GGP7GWP7BwbyV0dGyGLR2CRGZLZxufvUINY2emYtnchlkQT8zZtW4J9/344jPfNfxf2XJ4/jRH8Yf/3GiyBMs7Ev642ravDU0X6E4ql5Pw+5wGzjkpQ2wyXFjLFbAPg556/MdD/O+Q7O+UbO+caampoiRUfI7GhsEiMy27h8/uQA1i6g9Vmzz4GPXL0MH/vBfvQH43P+e9/d1YHHD/Tgk29ZBas0e2s3t03GukYv/nCETtVbDLONS1LaDJcUA7gWwK2MsbMAHgVwPWPsEX1DIoQQUkwvnxlecD/gqy+qwtbVNfiz772MwXBi1vtvf+40vre7A5+7qQ0euzzn59m0vBK/O9S7oBgJIcZjuKSYc/55znkz53wZgDsAPMM5/6DOYRFCCCmSwXAC/mAcSysdC36MWy9pxKUtFXjXv7+A9r7glPeJJRV89ucH8T8vn8eX3r4W1S7rvJ7j0uYK7OkYQjylLDhOQohx0Il2hBBCDOXVcyNYVece1wptvhhjeM/lzah1W/H+7S/h3Zc34/YrWtBa48RwJImn2/34j2dPYUWtC//wjrVwWOb/69Blk7C82ok9HUPYuppOGiPE7AydFHPOdwLYqXMYhBBCiui186NorXHm5bHesLImU/vbh7/6kVZn7LZJWNvgwV+9oRVtizyyeW2DB7tPDVJSTEgJMHRSTAghpPy8en4Eb1yVv01XlU4L7ty8BHfO0GZtodY1evHovvN5f1xCSPEZrqaYEEJI+VJVjsM9AVxU69I7lDm5qNaJc0NRBKk1GyGmR0kxIYQQwzg/HIXTIsFjm3sXCD1JgoCLap147fyo3qEQQhaJkmJCCCGGcbgngOXV+aknLpaVtW7sOzOsdxiEkEWipJgQQohhHOoKYMkiWrHpYUWNC/vPUVJMiNlRUkwIIcQwjvQEsaTKXElxa40TR3qC4JzrHQohZBEoKSaEkHlSVUp+CuV4X2hRh3boocJhgV0WcXYoqncohJBFoKSYEELmIRhPYTia1DuMkjQaTSKSTM/7ZDkjaK1x4WDXqN5hEEIWgZJiQgiZA845BsMJDIYSoKvkhdHeF8LSKicYW/hJdnpZUmnHkZ6pj5MmhJgDJcWEEDKLtKKiNxBHMEa9aAvpZH8ITRU2vcNYkKVVThzqCugdBiFkESgpJoSQGUSTaXSPxhBPKXqHUvKO94XQWGHXO4wFWVblxLFe2mxHiJlRUpxnCm3AIaQkcM4xFE6gLxCn13WRHO8P/f/s3XeAXFd58P/vuXf67M72XUmrbkuW5V5xwwhjwFQHQjGhBALByY/ETqXkDbwJJC+QBnYSEhtCqEEBTDHFFGOEsbEtW7Zc1KVV2VXb3qbfe8/vjzuz2l1t39m5U54P2ay8O+WZnTtnnjn3Oc+hvUyT4oaIHw10j6S9DkUIsUCSFBfYQCJDxnK8DkMIsQgZy+H4YJIhKZcoqkM98bJNipVSrG6MsO/UiNehCCEWSJLiAnO0pnskJafQhChTo2mLE4NJ+XBbZIOJDKmsTWM04HUoC7ayIcz+05IUC1GuJCleAhnLoT8uLZuEKCeOo+keTtE9nMKRD7VFl58lLsfOE3ntDWF2SwcKIcqWJMVLZCiZJZGxvA5DCDEHqaxN10CS0bS8Zr3S0TPK8rry7DyRt6ohwl6ZKRaibElSvIR6RtJYtpyCFaJUaa3pj2c4MZjEcuS16qVDPaMsK/OkuL0+zJHeuJTPCVGmJCleQrajZSWyECUqazucGEoxKLvTlYSD3aMsryvPRXZ50aCPsN/k5FDK61CEEAsgSfESS2VtBqS+WIiSMpq2OD6QJC29h0tGR0+87MsnwK0rPtA96nUYQogFkKS4CAZyq6qFEN5yHLc7jCymKy2Oo+kaTNIWK/+keHldiANSVyxEWZKkuEi6h9OyAYAQHkplbY4PJhlNyWK6UnNiKEks5CPkN70OZdGW14U5KDPFQpQlSYqLxHIcekelvlgILwwmMpwcSpGVha8l6UhvouzrifOW14U41CNJsRDlSJLiIoqnLdkhS4gismyHk0NJ+uMZ6QhQwg73xWmLBb0OoyBW1Ic53Bv3OgwhxAJIUlxk/fEMaUvqi4VYavG0xfHBJMmMvN5K3ZHeOC215V9PDNAYDTCathhJyQSIEOVGkuIi01rTPZzGkfpiIZaE1pre0TSnh1NSx18mDvfGWVYBi+wADKVYUSezxUKUI0mKPZC1HfqkTZsQBZe23MV0w1KmVFaOVlD5BMCyupAkxUKUIZ/XAVSrkVSWcMCkJihPgRCFMJLK0jsqtcPlxnE0XQOV0Y4tr602yBFJioUoOyWZkSmlVgFfAZYBDnCv1voub6MqvN6RNH5TEfRN3YZo295u7nm4g86BBKsaItx+43q2bGotcpSi2pX6cai1pi+ekdnhMtU9kiYSMCuiHVteW12IQz2SFIvCKvWxuBKUZFIMWMCfa62fVkrVAjuUUj/XWu/2OrBCcnL1xSvqw5iGGvv5tr3dfOqBPRzoGcVvGNSGTJ7pHOC9X3mKja01fOiWTfJCEEsqP/ge6B5hJGXREPHTXBOkeyTFX377WZqiAUYztucDs2U7dI+kZXOcMnasP8GyCmnHlre8LswjB3q9DkOUqamSX4CP3b8Lv6moD/vpHknxsft38XFgy6ZWSZgLpCSTYq31SeBk7t8jSqk9QDtQUUkxuPXF3SOpsR6d2/Z287H7d9E9ksJUCstx6Bl18BkKtGbf6RFJjsWS2ba3m0//ZC/7u0fxmwrHdtBK0RfPEPSZKAUDiSwjaYtzW2rOGpiLKZW16R5OYznSe7icHe2L01pbOfXEAMtiIY71J7wOQ5ShfA4wOfmNBkz8piIScNO2SMBHImNxz8MdwNQJ85u6Bnmso18S5XkoyaR4PKXUWuAy4IlJP38/8H6A1atXFz+wAkpmbPpG0zzfNcQdW58hnrFwHPAZivzieWvcKvqA4a7W9ioZETMr12Nz7APZcApTgXYg44Df0BjKGNt8xlBgOxql1NjA/Omf7C3qLMVwKkuf1A/PS6kel8f6EzTXBLwOo6BqQz4sRzOUyFIX8XsdTkkr1eNyKc00q3vPwx1TJr8dvXE2tNZMuJ2w36RrIDHldXpGUvz7tkOsbAhPObMsplbS3SeUUjXAfcCfaK2Hx/9Oa32v1vpKrfWVLS0t3gRYQD/fdZq//v4LJDK2OyusIOtopuooZTmalOXOMH/qgT3FD1bMqByPzW17u7lj6zOcGEySthy01hiGQgGWBqUgYztkcjvCBcwzQ4dlO+zvHqV7JDVh8N22t7vgcWqt6RlJ0zuSloR4nkr1uDzSG6e1QnoU5ymlWF4X4mi/1BXPplSPy6Uy/mzwVONl50CC8KT6+vx/JyeViSWzNisbIlNeZyRlYTkOkYBvbALDbyo+9cAe3nbv49zw6Yd4272PL8k4Xc5KNilWSvlxE+Kva62/43U8S23rk50YCgKmAq3wKTXtZR0NCnfV9oGeUTmoxaLkB+l4xiKf62YdN9n1GQqtwdaagGlgKvfsRXPNmdPdp4fTY7MU4wff/Gm9QrEdzYmhlGyKUGGO9idoraB2bHltsRBH+qSEQkw0flZ3qvFyVUNkyuR3XVOErK1JZCy0dr9nbc3tN66f8jppyyFoTkzxLNvhQE9xJjDKVUkmxUopBfwXsEdr/S9ex1MMJ4eTBH0GDdEADhpDKXzjnh2V+8r/228aKKXwG0bBkw9RXfKDdMhnAgp/biC1tEblPqgZShH2G6xrjlIf8eMz1ZmB2XFom1QTmj+tVyiW7biz2LKgruJ0DSQrbqYYoLU2yOGeUa/DECVmupng/Hh5+43rp0x+P/yq8/n46y+gtTbEUDJLa22Ij7/+ArZsap3yOqahzirdOT2Sxm8YSz6BUc5Ktab4euCdwPNKqZ25n/2V1vrH3oW0tJbHwvTF00QDPtpqFQOJDLaliIVM3nfDOn78/En2nnYHWL+pcLSDbYNhKJ4+NsC2vd1SJyTmbdvebp4+NoCjtbuw03bwmQZ+w50ttrVmQ0sNH37V+WPHV74ermsgwcqGCH5DkZ1U55M/rVcIGcvh9HCKrC0L6ipNImMxmraor8C629baEIf7pHxCTLSqIUL3SGqs/hcmjpdbNrXycZgwxo6vOZ7qfX6q69x6yQq+/fRxEhmLsN8kmbXJ2pqV9RM/gFq2w9PHBrjh0w/JYjxKNCnWWj/CmYnRira9o5+tT3ZypG+UeMamPuyjPhIg4AtiOYx9Erzj5o286rMPc7g3TsZ20BpMQ5ErP5YCejFv+bKJ/FkIrd1aSLTGAWIhH3ffdtlZx9SWTa0Tfpa/ncmD7+03rl90m6ChRJaBRAZH6ocrkrtpRxBjhnKxctUWC/LU0X6vwxAemGncu/3G9dOOl3mTx9i5mOo6F6+sn5AoB0xjbF0IwHAyy/HBFD5j6jZv1agkk+Jqsb2jn7seOoDPULTUBvHFMwwmLSwH1jVF+eObzp1wYH7olk1jBfqOo3G0JmuDYTC26K5aD2QxP/mFdfmFnbaj8ZkKA1CGor02PPaBbDbTzWzAzH01ZyL9h6tDZ3+iIksnwK0p7uxPeh2GKLLpWqrlx73ZZoILabYJjNMjKcDdljxfTtEzkuKOrc8QC/urcuZYkmIPbX2yE5+hxuqLGqNBwgEfTdEg//SWS6gNTTylmH8x3f61HVi2RuPOFvsMNWHRXTUdwGL+xi+syy+kG5sh1hql1ZwT4rypZinedu/j0/bVnOm2pf9w9TjWn6Clwtqx5TVEAgylsqSydkXt1idmNl1LtfHj3kJmggthckKuNbTXh8ZyjeFklr64e2ZudWOkKmeOS3KhXbU4OZwk5J/4FIT8BqeG3dmFkVT2rJX2Wza1cvnqBgJ+g4BpjC24k0V3Yq4mL6wzDIWpFD7TYE1TlMtXNxRkAJxtQclUhpJZTg6lJCGuEsf6EzTVVF7nCXDXe7TVBmUTjyqzkHGvmLZsauUb77+GX3/oJi5f3YBvXIeKfC/6kM+s2oV4khR7aHksTCo78c0/lXVYFjuz5WnvaIaMNfEy+ZWm+f9lbZuM5ZB19NiiOyGms//0MKeGUqQsxz1ubBuUJm05Z9W2LcZ0rYWmWoCntaZ7JEXfqPQfriZH+yq3fALcEoqj0patqsxn3PPa5K4VacsBDS25bkLDySynhlJsP9JfNT2NJSn20G1XrcJyNMmsjcb9bjma265aNXYZrTWnh1MTEoUtm1rZ2FqDoRQZy8F23DIKc9yiu2o4eMX8bdvbzWjaJmM7+A2FaShsBzKWJhIw5102MZPpWgtNTrozlsPxwSSjKasg9yvKR9dAYuwNuBI11wbplJniqjLXca8UbNnUOqHNWyRg0lwboDbkZziZ5cRQkoztEDRV1fQ0lqTYQ1evb+TOmzbQFA0ykrJoiga586YNXL2+ccLlsrZDfzwz4WcfumUTrbEQAZ+B33STG1AsqwtV3ekOMXf3PNxBY9SPQqEBn6nwmYqAz5iy08RiTB5wx/fVzBtJZTkxmDzrbIiofFprjg8mKzopbqkJclTaslWVuYx7pWR8OcXdt12G3zRJZKyxUgqFojUWqppSCllo57Gr1zeelQRPZSiZJeQ3iQbdp2z8ojtHa0zAUIrjg0kCpsFQIjPj7YnqtP/0MKmsg601OtdbOOQziAR9RVn9PF7faJqhpOxOV62GklnQEA1U7iK01togTx0d8DoMUWReLaRbrPEL8Y70JQiabkKcnznuHU1zpC/B2+59vGK7UshMcQENJjL05T5dLYWekfSEGbX8orvmmiAObp9ZUykytsNI2q740xxifiaXTvgNA9NQxMJ+NrTWFi0O29GcHEpKQlzl3B7FbiuoStUaC0n5hCgr+Znjq9c2srw+XHWlFJIUF9C/PnSQN3zuN9z7cMeSvOE7ufpiZ9zuYbffuJ6BhHtfygCNe7qjMeqv+NMcYn4ml06o3Kt/IJEtWr1b2rI5PpAkmZH+w9Wus7+y64nBnSk+PpiUxaOi7Iyvja6mUgpJigskbdn86LmTpLIOW5/s5O1feIIvPXqE0XRhFw9lbYeecbPRWza1Uhvy4c9twIDWGMrtWiGdKMR4+08PM5y0sLUma7udJ/yGoja0NKUTkyUzNicHpd2acHUNJGmu0HZseSG/SdhvThizRXnL2k5VbCo0vjY6lXuvWFEfQmvo6BnlWH+iInMMqSkukKDP5Od/diP/9tBBvvbEUeJpm688fpTv7jzOW65cyRsvW0k4YI5t63xyOMnyWJjbrlo1p5ri8eJpi8FEhvqI2/R+Q2st3SMpLFtzYiiJoRnburfaGm+LqeVLJxyt8RsKrRUOmljYz7rmmiW//6Fklv545qwZs0K8HkR5cnsUV+bGHeO11YUqeue+apOxHE4Pp3jycD//+1Qnp4ZSrGyI8IcvWc9Lz2/zOryCytdGv+3exyfmGKiKzTFkpriAakN+3vvidfzP+17E21+0mpDfYCRl8V+PHOF3vvAE//TTfXz2F/vpi6eJhXz0xdPc9dABtnf0z/u++uOZsVPQ+dMcp0dSKCD//6QThcjzqnTCcTTdw1P3H85vc16I14MoP0f7E7RU+EwxuCUUst1zZdne0c9nf3GAnpE00aDJyaEkf/W9F/jOji4Gcu/NlVQyU005hiTFS6A25Oe9N7jJ8VuuXEnQZzCUzPLjF07RPZImlbXR2t3lxmcotj7ZuaD76RlJY9nO2GkOrd26Y1/uNEdtyF9SO+kI73QOJGiKBllRH8KXK7VZ6tKJVNZ2+w9PU0I0fptzhVr060GUl+MV3qM4rzkakLZsFWa6sesrjx1lIJHh5FCSo30JukdSxNNW2SfI1ZRjSPnEEqqPBPiDl5zDm69Yyf9s7+S7zxzH0dAzmmEgkaUxGiAWMse2dZ4vy3E4NZyivT481okif4qjZyTN8cEkpqFY21h6O+mI4lrVEOFw7ygjKYuM7RAwDWpDviUrnRhMuMf4TG8GJ4eTxEITh6Dx25yXqtG0xa4Tw6yoD3PFmgavwylLWmtODKaqIiluqZVd7SrNXMYuR2tGUxajKQtDKcIBk5DPJBQwCPrKrw3h+BwjEvAxnMzS0TNK2nKIBEy27e2uiBIKmSkugqaaIH9807lsXhYb68lpOZrukTSH+5IETNNdJLcAGcuhZ8RdxHH7jesZSmY5PpgkazsowLI1ffFMxRXDi/m5dn0jPaMZMraDoSBjO/SMZri2wPW7lu1waig1Zf3wZHPZ5txrjtYc7o3z4+dP8k8/3cd7vvQkt/7bo/zxN57hi48c9jq8stUfz+AzFZFA5c/LtNQGOSZt2SrKfMcuR2viaYu+eJrjA0mO9SXoHU2XXZlFvoyiZyQ11qJNAdGgWTEt2ip/RCoh77p2DXc9dIBYyMdoxmYkZWE7mmMDCd775af43WvX8JLzWjDm2bdzNG1hjqbZsqmVlpogoym3w0DANGipDWIabs1PJXyKEwvzWEc/rbUBhpNnZopjYR+PdfRzR4HuI552W/fM9QPebVet4q6HDpDM2oT8Bqmsc9Y258U2ksqy5+QIu08Os/vEMHtODRNPn73S3FCQyMi21AuV71FcDVprg3QNlPbZDzE/ix27LMdhOOkwnMyilCLoM9wvv0nIZ+AzS3O+Mr+5xx1bn8HRmpDPpKU2SG3ITyJjVUSesaCkWCn1PG5L3ClprS9ecEQV7Or1jdzJBrY+2YkeTtJeFyboN3iua4hj/Qk+8aM9fH37Md597VquP7dpXk3th5JZTEMxkrY4t7WGkZSboMgOdwLO7GSXT4iba4LUhnwFqQVzHE1vPM1oan5J4vjXw6nhJMuK3H3C0ZqjfQl2ncglwCeHOTrNjF5d2M/5y2u5YEWM85fHuGptI2uaokWJsxJ1DSSrYpEduGcK++LuxksBX2kmO2J+Cjl2aa1JZW23zVtufwO/aRAJuDvYhvylVWqxZVMrsbCf1Y0RlFJjZRQZ26FrIFn2ZRQLnSl+be77B3Lfv5r7/nZAzhPNYKptnTt6Rvnv3xzh0YN9dPTE+dj9u9jYVsN7rl/L1Wsb55wc98czLI+F6BxI0BfPYKDGdrizHF32B6tYmPHt2EylxtrqNFmBRdcUpy2b7uE0WXthvYfnus15IQwns+w55SbAu08Ms/fUCPEpNhExFKxvqeGC5THOXxFj8/Ja2uvDE16H1XDafyl1DlRHOzYA01A0RQOcGEyytlk+SFWKpRy7srbDUNJhKDeTHPAZBEz3K+h3Z5W93AlyVUOkYlu0LWhk11ofBVBKXa+1vn7crz6slHoU+HghgqsW61tq+MStF7L/9AhffPQI2w/3s//0KB/5zgtsXh7j925Yy+Wr57ag57cvX8nH7n8BrTXKVGg9cYe7cj1QxcLl27H1jWbPtGNz3HZsn1pEO7ahRJb+xOy1w16wHc3Rvji7T464SfDJ4WnrOuvDfjaviLF5eYzNK2Kct6yWcInNzlSaY31uN5Rq0ZqbrJCkWMyX1pp01iY9bsOQfKKcL7uIBHyYRvGS5NtvXM/H7t9F9/gWbdpt0Vbu5ZqLne6IKqVu0Fo/AqCUug6QV/0CbWyr5VNvvIhdJ4b44qNHeObYILtPDvMX33qOS1fV83vXr+XC9roZb+Pq9Y1Egz6SGYt01kEpcqc4LA6cHi7SIxGlpHMgQcA0MBSkLXdG129Abdg/74FLa81o2mIomSVjlc7OdMPJrFsHfPLMLHBimlngc1pqxhLgC1bEWF4X8nTWpRp1DiR40bomr8MompYaWWwnCufsRDmN3zQI+AxMQ+EzFKah3OQ59/NCytcW3/61He4ZSMBQqiLKNRebFP8e8N9KqTrcGuOh3M/EIlywoo5/fvMl7Owc5IuPHOaFE8Ps7Bzkjq07uWptA++5fi2blsWmvf6axihdA3EsJ+ue1sh1GyiFEgrH0Thak7EdspbG1hrb0Wj0hCp1DWM9EVfUl043gnJUEzA52BPHzC3o0BpsrWmOzv30tWU7DKcsRlLZBXdKKRTb0Rzpi4/NAO8+MUznNAuZGiJ+Ni9364AvWBFjo8wCl4TO/gSvu3iF12EUTVNNkGPSlk0soaztTFvG5jPcsouAaWCabqK82BKMl5zXwiUr6znSN0p/PIOhyU282CWRayzUgpNipZQJvERrfYlSKgYorfVQ4UITl66q567bLuWpowN88dEj7Ds1wpNHBnjyyADXn9PEu69fyzktZ9eE3nbVKj56/wvufxhucomGuoiPf/vlQS5f65ZiGEqNnfoO+82CrXh1HE3KssnaGiuXjGdtB8t2E2JRXGMDX77oC0AzpwHRcTQDiQzDKe8a0A8ls+w5OewuiDs5zN6TIySzU88Cn9taM5YAb14us8ClSGvNyaHq6FGc11ob5GDPqNdhiCplOQ5W2mHyFjKGUhhKYZoKv6nwGwaG4U6kKXKTU447ieLkJrDyZwgtR/PGy9on5BpOBZRrLjgp1lrbSqlbgc9oreW8/BJRSnHV2kauXNPAYx19/PejRzjUE+fRQ308eqiPl2xs4d3XrZmwEv7q9Y3U5EooLEfjNw0aowEiAXfnmd5cX+OpGErhMxV+051RzNoOWoNpuolF1nLQgM9QYy8SpdwFfRo951nE7R39bH2yk5PDSZYXuetAtRlJW7TXh+gZSY91n1hWH5p2pzlwd6MbTmaJF7mPpu24fYHzM8C7Tw5P284qf4qwMRrgdRev4LcuW1FyK7XF2XpHM4T8ZlU9V621QR7aV/49XKvdtr3dfG7bIY72xyvifcvJJbuWA+ns/K8/Va7RmmvRVq673C22fOJRpdS/Af8LZz6EaK2fXuTtikmUUlx3TjPXrG/i1wd6+dKjRzjan+BX+3v49YEeXnZ+G++6dg3tuVKDNY1R+uJpwn6T0bRFfzzDqSGHkN9ke0f/tC9kR2sylj6rXnRy+WjWPpMoaa2x5pE4be/o566HDuAzFLGQj754mrseOsCdbCjrAaZU1QRMjvQnsGwHv2lQHwmgcescu0dSBHJnCBSKtGWTzNpFK5EYSri1wLtODLH75Ah7Tw2f1RQf3BX857bUsHlFjKBp8It9pwmaBuGASSrr8IPnTrC+OSrHTxnoHEjQWkWzxOBu4HFcehWXtW17u/nY/bswDeR9a5x8rmHnzioeH0ziM9Nlu5PuYpPi63LfP447056fcb9pkbcrpmEoxUs2tnDDuc38cl83X/7NUY4PJvn57tP8Ys9pbrlwGe+4Zs1Yc/H+eJrBXO9DBUQChucv5PH7xoNbupHM2mx9srOqB5elsG1vN33xDJatczscOpweThEL+/n/XnLuvHsLL4btaDp6Rt2OELmZ4OODUycKjdHAWE/gzctrOa+tlmDuePmz/32WkM+U46dMdQ0kq6p0Atw+12nLZiSVpTbk9zocsQD3PNyB31QEfSaW7ci4k3PbVav49E/3MpzMYuTKLsbvpFtuJRSLqSm+Gvig1vpJpdQFwC3AHq31jwsWnZiWaShuPr+Nl57Xyk93neKrjx/l9HCaHz9/ip/tOs1rLl7Oe65dy10PHcDREPS5JRTRgM/zF/Jc9o0XhXHPwx3Ewn6iQR+nh1NkbQfTUDREAkv+/A8mMuzKbYqx+6TbEWLaWeBWty9wvjVaWyw4bS2wHD/lrbM/QdM8FnlWAqUUbbEQnf1JNq+QpLgcdQ4kqA/7GX8STcYdt4SiIRIgnrZwcjvptsbKtzXbQne0+7/AqwCfUurnwNXAr3D7FF+mtf77AsYoZmAaildftJyXb27jx8+f5GtPHKNvNMP3d57gAd8pfIZiTVOEdNahP57h9HAKv2kwmlpAAVGBLI+Fx0o78mbaN14sXOdAAlO5dZzZXPlEQ8Rf8C2KbUdzqGf0TEeIk8OcGExNedmmmoDbEi23IG5Da83YLPBcyPFT3o72JapuphigrdZty7Z5xfSdg0Tpym9YEfTJuDNZPGPRXBNgIJElYzv0jKRprgmUZV3xQmeK3wRcCgSBU8BKrfWwUuofgScASYqLzG8a3HppO7dcsIwfPHeSb2w/5h6gwOHeBAowFRiGcjtBOHrG2uKltNh948XcjW/Hlt/N7vRwmjWLrPfqj2cmdITYf2qE1BR9i32GYkNbriNEbia4tXb6WeC5kOOnvB3rj/OSjeU1e1QIzbVBOqVXcdnKb1jhaAufoWTcGSfqNznan3A7WeTeZ44Ppji3pfy2rVhoUmxprW0goZQ6lO8+obVOKqUK0tFfKXULcBdgAl/QWn+qELdb6YJ+kzddsZLXXLyc7z59nK8/cYxk1kYDlgbD0RhAfcTnWQlFIfeNFzOb0I4tTwPzSEot26GjN+4mwLkk+OTQ1LPAzblZ4Hw98Ma22oI3jpfjp7wdH0hW3UI7cBe2Hu2b3BRLlIv8hhWf23aIY/1xT8Yd23E7PFmOk/uusewzP7McjW27/f8te9zP8tez9dh/n30bzrjf6Qn3deY+8pd3xv1ec3I4jZ3bVwAgP5leju0wF5oUZ5RSEa11Argi/8PcJh6LTopzPZD/HXg50AU8qZS6X2u9e7G3XS3CfpPfedFqbr10BW++57GxWk5Hg1bu9xND3s1aLOW+8eKMfDu23tEMaStXPlETmLF8oj+emVAGse/UyNhOeOP5TcWG1ppcHXAdm5fX0hoLLeXDGSPHT3myHfdMRXNNFSbFtSGeONzndRhiEZprg9y0qZXBpLt4ed9pd9FwdlyCac+UeGqNnTtTa01OPm1nLJk9k4A6E26v1Lv85+MzlGJZXXDGtp+laqFJ8Y1a6zSA1nr8u6Uf+N1FR+XWKB/UWncAKKW2ArcCkhTPUzToY1NbjJ6RFEOpLKNpG62hL+6uFP3Wji5ef/HyedV0ivKxqiHC4d6JmwZkLIeVDe5pLct2ONQTHyuD2DPDLHBrbdDtBrHC7QixobXws8Cisp0eTlEb8lXlcdNaG+So7GpX1h471MenfrLX6zDmLd/TPf/d/beBz5z4c5+R2ybanOKyuZ9PuFzu97/a38NIKks2t0GXu7Odw7rmszcXK3ULSorzCfEUP+8FehcVkasd6Bz3313Ai8ZfQCn1fuD9AKtXry7AXVaufMuUZMbGl5sldnC//8e2Q3zzqU7efvVqXn3R8qp8syq0Ujo2r13fyPYj/RjK3fEtYznu4pA6hzu3PsO+06Nn9aSG/Cxw7VgZxAUrYlW5OKqSlMJx2dmfoK1IZxNKTWssyKmhFLajMY3yO628VErhuJyrppoAG9tqcLSbaE5INk1jws8mJ575y/nySaU56fq5xHNiUjouGc3tPOcbl6Qa+dvL/d5nGhPvxzTcNmlLXMZQEzD58uNHMRT4DHe3u57RDL9zdfmdzVtsn+KlMtUzOOHMgdb6XuBegCuvvLJkziqE/SYKxfgxz1CKgM99cWjtlnPqXP2NkTtY83U6+d/nD2JDgZPbZtGyHZzc/uKWo0lbzpx2G5vcMiXoM6gL+xlNW8TTNn2jGe5+6CBbn+zkndes4ZUXtBVsy+dqVErH5mMd/bTU+OkZzWKPy31fODFxE8rW2qDbESLXEu3c1hr5gFRhSuG4PNZfnZ0nAII+k9qQ2xpxRb10LMgrheNyrt54+UpeecEyTg9PfTatWj3TOURT1M9o2iZrOwR9BrGwj8c6+rnD6+DmqVST4i5g/JLOlcAJj2KZl9qQn9rcRMi2vd3c83AHnQMJVjVEuP3G9QXt2ec4mqzjkLEcsrYmmbVJZ+0pLxvPWKxpihBP2wwkMvSOpvGbBrGgyQ0bW/nJrlN0j6T555/v5xtPHuNd167lZZtaZUajzHUOJAj6TBwnM+HnflPxW5e2c8EKNxEutxpP2Sa8PHX2J2iuqa4exeO1xUIc609IUlymKm2b50I5OZzEbxrAmfwjYBpl2ZKtVKeCngQ2KKXWKaUCwG3A/R7HNC/5LSG7R1LUh/10j6T42P272La3u2D3YRgqN/vgpzEaoL0+THtDmMZoAL9pTDhlsjwWZiCeoXskhWW7M9RZ2yFhObz43Ga+9J6reMXmNgwFJwZTfOqBvbz3y0+xbV/32IpSUX5qAibHB1O5lmy5tnzAqvowf7jlHG7c2FKWCfFdDx2gL56esN3q9o5+r0MTszhSpT2K81pjQY5JXXFZyr+n94ymZNyZJOo3OT2cxrL1hJZs0UD5rVUqyaRYa20BfwT8FNgDfFNrvcvbqOYnvyVkJOBDKfe733R3eFlKQZ9JfSTAqsYIaxojNNcGCfgMbrtqFUO5LX2Vkd+TW1EfdluztdeH+fCrNvHF372Kl57XArinOj/+wz28/6s7ePRg75xKNURpyX8wMk237swwlNuNrQxb5eSN3yZc4X73GYqtT3bOfmXhKbd8ojprigGaa4IckbZsZSn/nh72+2TcmWx868/8F+XZkq0kk2IArfWPtdYbtdbnlOMOeZ0DiQk7boFbb1zM0wmGoYiF/LTXh3nlhcuoCfrwGQrH0aA1SsFAIsuuk0Njn3ZXN0X46Gs38/l3XcH15zQB0NET56Pf38X/9/Vn2H64X5LjMpJvyYbWpC1N1tYYhmIgPuVa2bJwcjhJyD9x6JLtVstD10CCtmqeKa4NcrhXkuJyNNV7uow7rv54OrcxmPs+g9a014fKsiVbySbF5W5VQ4TkpPreZNZmZcPidhJbCKUUDdEAG9tqaYuFaK0NoXEX++U/1E0+DXROSw2f+K0L+Y+3X87V69yaqX2nR/jwd57nzq07eebYQNEfh5i/VQ0R0paDAwQMRcBUaK0ZzdiznvZTSuE3jVytWOlYHguP9d3Ok+1WS18yYzOctGiIVm9N8bJYiKOyq11Zmuo9vRzHnfHjesB35iscMIkGfWO/C/lNIgEfNUEfNSEfkYCPYO5n4YD7PRLwsePIAImMjdaagKkIGAoHtyWbF/nOYpXqQruyl98SMpGxCPtNklmbrK25/cb1nsX0hy85h4/dv4u+eMo9u6EArWipDWIoNeUOd+ctq+VTb7yIF44P8d+/OcIzxwZ54cQwf/6t57h0VT2/d/1aLmyv8+LhiDm4/cb13P61HYBbNuPoiWUz45/vgM/IDXTmWDufPNvRpLI2yaxNKmtP2catWGSb5/LUOZCgLRYc67hTjdpiIbokKS5L5bTNc8BnEPSZ+HMlc/58azjl/nchfeeZ4zTVBOgbzbqdswz3/WIgkeVTHuY7CyVJ8RLJbwl5z8MddA0kWLkE3ScWGtPtX9uBk/tU1xgNEA340OgZTwNd2F7HP7/5Ep45NsB/P3qEF04Ms7NzkDu27uTqtQ285/p1nLestngPRszJlk2t1IZ8JNIWGdvd0a4xGiASMDk1nEQpRTRoEgv5Cc2wgYtpKKJBH9GgO2Tkk+R42iKemyUoFtnmuTwd60sUbcfDUlUb8mE5msFEhvpI9c6Yl6NS2OZ5Kj7DneUNmG7iG/KbRe0a1TmQoCkaJOgz6RlJu+8zhiIS9Hma7yyUJMVLaMum1pI7KLZsauXy1Q10j6TIWg49o2lOD6cwDMXKutlPA122uoFLV9Xz1NEBvvjIEfadHmH7kQG2Hxng+nOaePf1azmnpfx2salkG1prOdw7ylAyS9Z26I9nSGdN1jTVsLoxsqABdHyS7Dia0YzFaMqac+/sxZJtnsvP0SruUZynlGJ5XYijfQlJisvQlk2tXLWu0dM+xflyh6DPJOR3v3upJmBysGcU29EETIMVdWF8pqK1TBfUllaxoCiK229cz1Ayy4nc7koKsG3NQDI7p/YySimuWtvI595+GZ+49QLWt7hbBj96qI/f/8oOPv6D3RyVFdYl49r1jfSMZsjaDkrhJsaJLC/Z2FyQGYX8gs4V9WHWNkVojYVk23BxliO9cVrKrP3fUmiLhaQDhZgTQylqgj6aaoKsqA+zrjnKyoYIrbUh6sJ+zxPibXu76YtnsGw3j8jaDscHkwwls56Wii6GzBRXoS2bWmmpCTKasrC1JuAzaYgGUDBlXfF0lFJcf24z157TxMP7e/jSb45yrD/Btv09PHygh5vPb+Od166hXRrVe+qxjn5aawMMJbJkbIegz1yy3YZUbhCvCfrIWA5DySyJjIXtSMeSanekL86L1jV5HYbnWmuDHJEOFGIKSrnbM0dyi96CPqOk25rd83AHsbCfaNA3VjrhMxQtNcGSO0s+V5IUV6mRtMW5rTWMpCx6cyUUfkMxmsrO+7YMpdhyXisv3tDCQ3u7+fJjRzgxmOJnu0/z4J7T3HLhMt55zRraqrye0Cv5mq+AadI76g5cw0mLA6eHZ7/yIgR8Bi21QbQOkMjYDKeyJDNT77goKt/RvgSvv2SF12F4ri0WoqNHkmLhvneG/CYBn0HIb7i910s4CR5v295unj42kFufZNBcEyQW9qO1Zig5/zyiVEhSXKVWNUQ43DtKXzyDgbsqNWO7q2m3d/QvqF7TNBQv39zGS89r4We7T/OVx47SPZLmx8+f4ue7T/Pqi5bz9hetLrsd1MrdTM/1tr3dS/6J3l3M59YfZyyH0bTFSCors8dVxLIdTg2lyrbOsJCWxUI8dqjP6zCER4J+k3DuK+Qv7Zng6eR398u3dLVszYkhd6G+z1Rl2YotT2qKq9TtN65nIOF+msvvcGcog8aon2/uWNwOPT7T4NUXLecrv3c1d9x0Lk3RAFlb8/2dJ3jHf23nP7YdYiCRKcCjEHMx1XOtUDRG/Uu+w+JkAZ/b/WJ1o1t7HC7DbUDF/J0cSlEX9hPwyVtOW530Kq4mPsOgJuSjNRZiTVOU9vowjdEA4UD5zApPlt/db1ldCFD5/+P0SMrz1rOLJTPFVWp8q66sozFwT+X0xbP0x4fYcWSAK9Y2LOo+Aj6D37qsnZaaIP/xq0OcHE6RsRy+taOLHzx3gjde1s5br1pFbchfmAclppR/rocTGdJZd3Y26DMImEZRd1gcL197/NThfv7zV4c41p+gLRbirVd63+JIFN6RvnjuDVTUh/1kbUfaspW57R39bH2yk5PDSZZPas+WT4SjQdPzxXCFNrlsoj7sI56xSVsOSis+/voLyraeGGSmuKptaK1leX2YFXVhHM7scGcoxd0PHeCpI7N3opjN9o5+PverQ5iGYn1ThLqwD6XcnYD+Z3snv/P5J/jyb44QL8PtIMtJczSAgyJgGgR9BlrD8cEUUQ9navOn4HpG0zRGAwynsvzbtoPsPDZYtjMoYmpHeuO0xaRsCtwPhO31YdnuuYxt7+jnrocO0BdPEwv56IunufuhA+w+MczKhgirmyI0RgMVmRBPLpsYSGZprgmypinK5asbyjohBkmKq9rtN64na2tOj4zb4Q73lEjAZ3DfjuOL3n1q65Od+AxF2G9iGgZttSFW1IVYFgsRDZjEMzZffuwob//CE/zPE8fO2kZTFMZYkqnGfY3/uQfyp+AiAR9Kud+DPoNv7ehidWOEpmiw5LaYFgvT0RuXeuJxltWFJCkuY2PvawETn2EQC/sJB0y+/sSxii4RquSyibzKffbErLZsauXjr78ArcHRGp+hWFEfojbkJ+w3OTGUpC0WWlTidHI4Scg/8TCLBExA8/X3vYjfuXoVIb/BcMriC48c5h1feIJv7egiLclxQY2kLdrrQ/gMhe24z3V7fYhRD2foOwcShCf1Mw77TboGEpiGoi7iZ1VjhGV1Untc7jp6pHxivNbaoHSgKFNKuUlgTdCX20XOwFBqbOyqZPkxuzbkZ0Xu/cTRGq0p+7KJPEmKq1x+h7s1TVGaa4L0jKTZe2qYgz2jRAMm4YBJc83C696Wx8Kkss6En6WyDstiYWJhP+978Xq+/r4X8eYrVhLwGQwksvzHtkO844vb+f7O42QsZ5pbFvOxqiGCL9c2J2AaZGyH0yNpT8snVjVEzjozkMzaZ61cjgR8LK8L094Qpibok9KKMnS4N84yack4ZnldmIPdo16HIRYgEvCxtilKxnYmjEVTjV2VZNveboaTWfaeGqGjZxStYX1LTcWUTeRJUizGdrg7Pph0dz3DrRXqi2fYtreb2pCfhgUuCLntqlVYjiaZtdG43y1Hc9tVq8Yu0xAJ8IdbzuFr772aWy9dgc9Q9I1muOsXB/nd/97O/z55DMuW5HgxZnuOvYopa2sSGQut3e8znYIL+kxaYyFWNoSpC/slOS4T+XZs0qf8jBX1YQ71SFJcruY7dpW7fC1xNGiigIztcGIoSU8FlU3kSVIsxna48xkKDfhNg5UN7kxuvmVXQzRATXD+zUquXt/InTdtoCkaZCRl0RQNcudNG6bsMNBcE+TOl23gK++9mtdctBxDwenhNB+673ke8ihxqxRzeY69iOnjr7+A1toQQ8ksrbWhOZ2C85sGTTVBVjdGaIgE8BkyjJWyzoEkjVFpxzbe8roQx/oT0qu7TC107CpX+Vri5poQ7Q1hAqaBozWJjF1xj1tasgng7B3ujg8mCZgGQ+P6CTfXBElbDtl5ztpevb5xXm22lsVC/PkrNnLbVav4yuNHOTmU5Obz2+Z1n+Jsc3mOi23LptYFD6imoWiIBqiP+IlnbEZkx7yS1NEzygrZ6n2CkN8kFvZzYjDJqsbKPeVeyRYzdpWLbXu7uefhDrYf6SfkO7NrXW3ozM51lfY3kI/uAnDrO3tH05wYSmLZemzXs5G0PXZ63TDUohfezUd7Q5iPvGoT9/3hdRiGnCpfrLk8x+Uo3/N4eV2YFfVhogs4oyGWzuHeuJROTGFlfZiDUkIhSlS+ZKJ7JEXQVGMlE8O5LZwrtYZakmIBnL3rmeU4WLbGsh3u2PrMWNIU8Bk0LWLh3UJUWq9Hr4x/jh3tkLYdsrZGa82nHtjjcXSFEfKbtMXcU3w1IVmUVwoOnB6VRXZTWFYX4pAsthMlZtvebt527+Pc/rUddI+ksGxNayyEyvXx7B1NV3QNtSTFAjiz65nfUGQsB9txT0/7TEU8Y/Gx+3eNJcaxkF92oStD+edYaU3WATT4czPwB3pGy3q2eLKgz6S1NsSq3KK8xfbbFgt3UMonprSiPszeUyNehyHEmPGzw7bj4DiaE0NJtIYV9SH8hiJlORVdQy3nGcWYDa21dI+kODXkfjo0cj0IQz4Dv6m45+GOsRdBc00A23FX3U420/aXwlsbWmt5pnOAoGKsJMXRGr+a+PwWSr4mrXMgwaqGCLffuL6oA6kvtyivPhJgMJFhNG3J4qYik5riqa2sDxdk11BRerwe9xZq/IZKQZ+J5WiUdmeH17fUYBqK1toQ33j/NV6HumRkpliMybeZSVsOKE3WtslYDinLbal04PTw2GWVUrTWBs9aUT7V9pd3PXSA7R0y+JeC/HOc/1/+Oc46mqePDRR0tnj8rEN92E/3SGrCGYdiMg011rGiqUZ2yiuWgXiGjOXQEJEzS5O1N4Q51BNHa/mQVklKadybi3y5xA2ffoinjw2MtT9tqQ2iNWg0Gdup6JKJ8eSdQYzJt5mJBEwylh4rofAbUy/IMnKfGsfXbY7f1lnhfvcZiq1PdnrxkMQkWza1srG1BkNNLJMxc9t1FnLwnmob5/wZB68opagLy055xXKwZ5SVDRGp7Z5CbciP31ScGk55HYoooFIc96YzOYFXwPHBFMPJ7NiudYZSGEpVdMnEeJIUiwm2bGrl7tsuI+Az8JluTfF0i+7AXXjXGD2z8G6qbZ1DfoNTw8miPQYxsw/dsonWWIhArizGzG39DHBiMHnWc7xQM23jXAom7JQni/KWxIHTo7Q3SOnEdNY0RaWuuMKU+rg3fmb4jq3PkLHssQQ+vxX76ZEUWmu3XCIW4p53XME33n9NxSfEIEmxmMJ8Ft0B1IX9Y22wZtrWWZSG/BkBrd16YrRGKYXWYBpM+RwvxFy3cfaaLMpbOvtODbO8TjpPTGdFfZh9khRXlFIe9ybPDMczFn3xzFibtdqQn/b6EFpTFZuSTEUW2okpTV5052hNxtagoXskxace2DPhhdJSE+ThfT0MJzMcH0ziMxTNNQFMwzhrW2fhvS2bWrl8dcPYc0xuYWXWdtD6zIzx3bddNqcBcaqFJbffuJ6P3b+LRMYi7DdJZu2SrknLL8priAQYSVkMJbNYjmwvvhh7To1w03nV84Y6Xysbwuw+MTz7BUXZmMu4V6yFeJPvZyCeHivtAAj5TDK2Q+9omljYrfv3mQaXr26o6MV0M5GZYjGl8YvubO0uxNIafIbCcfRZLbwe3t/D3Q8dIOtolsWCoODUcBq/aUy7rbPw1lQLKy0HDKXmNWM83cISoCy3QjUMRV3Ez6rGMC1TLCYVc3ewe5SVUj4xrTWNEXaflKS4ksy2BXSxFuJNdT8HekbHFtKBu5gODWnLQWtdNYvpZlJyM8VKqX8EXgdkgEPAe7TWg54GVYW2bGrl48AdW59hOGWhAL9poLUeS5Df+5WniPgNlFIkszamUrTUBqkJ+qkJ+klmbWIhvyTEJWr8cxzPWGMfevymQda2x2aM//DrO1jbFGUkbbGqIcK16xt5rKN/bPZhMJGZMPsQCfhIZCzuebijrOvQlFLU5npyJzLuzLFsIz13/fEMacuesOZATLSyIUJnf4K0ZcsmRRVkpi2gxy/Eg4nj5fjEeaqZ5JlmmGebFY4EfPgNg9MjaWJh9zVZG/LTXGsTT9sMJbOsLKP2cUul5JJi4OfAR7TWllLq08BHgA95HFNVyi+6e+9XnsJUoLXD+HJh29GMpG1MA2wHtKE5PZymtTZAJOCTBXZlIP8cf+z+XZwYTGIajM0Y+wwFaJJZzYHuUdrrQxzuHWX7kX5aawM0RYN0j6Q40pdgZf3EutFSWlhSCJGAj0jAR9qyGUpkkbLj2e09OczapqgsYJxBwGewvC7EgdOjXNhe53U4ogg6BxLUhye2KBw/XuZneP2mmjCT/KauQb799PGzfv7x3G1Mvs6RvjgrJ/UHb4sF6RpMTSjt8Jsmd992cVUnwuOV3HlBrfXPtNb5HSEeB1Z6GU+1G9/Cy3Lctl2T3+K0BqXcxFgpd4YIJQvsysX4VnyWo3HGzRjb2n2+TUPRO5phJGVhKBhOWhPaDZ0eTk+4zVJZWFJoQZ9JayxEk8x+zmr3yWEpnZiDNU1RqSuuIrMtxJuupdsXHjk8bau3Ka+TmxUez2cabGytKbuStmIquaR4kt8DHpjqF0qp9yulnlJKPdXT01PksKpLvoWXaSgCPsXkVvNag0+BBrR2G31nLKdqF9iV47GZnzFur4+M1RQ7+kwduVK4z6vtYOT+nddWGyTruM3dq6UurRxnP4t9XO46McSqxsr7YFRoqxsjPNs16HUYninH8XIx8ms5phsvp2vpFs/Y07Z6m+o6bbHglPfzoVs28Y33X8OvP3RTWZe3LRVPkmKl1INKqRem+Lp13GX+D2ABX5/qNrTW92qtr9RaX9nS0lKs0KvS5JlEQ7mJUm6XYJQCwzAImgrDUJiGwfK6MB98xXlVWU9crsfm5OfZZyhCPgMj164tYBoETAMn9+88n2mwoUVmH0pdsY/L3SdGWCNJ8azWt0R5tnPQ6zA8U67j5ULNthBvupnkaMCcdoZ5quvIrPDCeFJTrLW+eabfK6V+F3gt8DJdJntglute53M1vvY0Y9n0xTPkp4yVAsfRNNcG8Jvm2AvPsh2ODybHNoYQpW/88+w3Ve45TIGGZbEgacuhZzRDLOxDaz3Wbuijr9lcNsd7pb9WS0HasjnSF2d1Y9TrUEre2qYoB3tGyViOdDqpAHMZX2ZaiDddS7f33bCObz99fNpWb1Nd56Ov2SRj2zyV3CtQKXUL7sK612uty2KlTrntdb5Q+U+465prqAv5qAn6qA2aRAM+6iJ+1jbVTPgk6jMNmmuCHkct5mv8TIaj4dyWKBtaa3A0rGuu4c6bzmVtU01Zzj5Uy2vVawdOj7K8LiRJ3hyE/CbL68LsPSV1xeWuEOPLdDPJd9y8cdoZ5tlmn8XclWL3iX8DgsDPc3V7j2ut/8DbkGY2lxYrlWKmT7hTiQbd5Hk0bc1+YVEyZnue7yhiLIVUTa9VL71wfIi1zTJLPFfntER55tggF6+s9zoUsQiFGl+mG39nGpfn+94splZySbHW+lyvY5iv2VqsVLummiCprCO7gwnPyWu1OJ45Nij1xPNwTksNTx7p53evW+t1KGIRZHwpf3JuqwBKea/zUmAaiuZaaWElvCev1eLY2TnIua01XodRNja01bLj6IDXYYhFkvGl/ElSXACztVgR7mmkmlDJnZgQVUZeq0svmZFFdvO1oi5EMmtzfFA2OypnMr6UP0mKC0CK3OemORrEZ8ghJ7wjr9Wl9/zxIVY3RmSR3Twopdi8PMYTHX1ehyIWQcaX8idTdwUiRe6zMwxFS22Qk0MyGyK8I6/VpfXUkX42tEnpxHyd11bLIwd6eePlsolrOZPxpbzJR3lRVOGASd2khQhCiMqx/Ug/G1prvQ6j7FzUXscjB3spk9b8QlQkSYpF0TVGAwQnbUkphCh/jqN5+ugAG9skKZ6vZXUhDAUHuke9DkWIqiVJsSg6pRRttUHM/D7RQoiKsPfUCLUhP41R6TYzX0opLllZzy/2nPY6FCGqliTFwhM+06C1NuR1GEKIAnq8o4/Ny2WWeKEuXd3AT3dJUiyEVyQpFp4JB0waIjKjJESl+NX+Hs5fHvM6jLJ1wYoYh3pGOTWU8joUIaqSJMXCUw3RACGpLxai7GUsh6eO9nNhe53XoZQtv2lw1doGfvjcCa9DEaIqSVIsPNdSG8RQUl8sRDl76mg/7fURakPSXWYxrl3fzDef6pQuFEJ4QJJi4Tm/adBUI2UUQpSzn+06zSWrZJZ4sTaviDGasnimc9DrUISoOpIUi5JQG/JTE5S9ZIQoR1prfr77NFesbvA6lLJnKMXLzm/j3l91eB2KEFVHkmJRMpprgvhNOSSFKDcvHB9Ga83qxojXoVSEmza18nhHH/tPj3gdihBVRTIQUTLy20ALIcrL9589zovWN6FkbUBBhPwmr790BX97/y6pLRaiiCQpFiUl5DeplzZtQpQNy3b43jPHue6cJq9DqSgvP7+NE0MpvrH9mNehCFE1JCkWJach4pdtoIUoE9v29dBcE2Rlg5ROFJLPNPjAlnP5h5/s4+H9PV6HI0RVkJVNouQopWitDXJ8IIkjpw6FKGlffPQwLz2v1eswKlJ7Q5g7b97AnVuf4R0vWsN7blg3toW21prukTS7Tgyx99QIHT1x+kbTOBqaagKcvyzGtec0ccGKmJS1CDFHkhSLkuQ3DZprg3QPy85OQpSqPSeH2XtqhD94yTleh1KxNi2L8YlbL+TbT3dxw6cfoj4SwGco+uJp/IbBuuYo7Q1h2mIhVjdGUMBQKstTR/v570cPE/AZvOf6tbz1qtWyUZIQs5CkWJSsmqCPVFg2AhCiVH3m5/t59UXLpGvMEmuqCXL7jefw3hvW0TOSxnGgLuynJjTzW7jWmn2nRvjBcyf53C8P8eFXb+K3Lm2XmWMhpiFJsShpTVFZdCdEKdpxtJ9njg3wj2++xOtQqobPMFheF57z5ZVSbFoeY9PyGPtOjfDvvzzE1x8/xqd++2LOba1ZwkiFKE/y8V6UNJnREKL0ZCyHD9/3PG+9ajVBn5ySLwfnLavlb193ARe21/Hb//Eb/vWhA2Rtx+uwhCgpkhQLIYSYl0//ZC/1Eb+0YSszhqF45QXL+MStF/LLvd285u5fs1O2kxZijCTFQggh5mzr9mP86LmT/P6L18uZnDLVUhvkL15xHi/fvIzf+9KTfOi+5+gdTXsdlhCek6RYCCHErLTWfPGRw/zTz/bxl688j9qQLIItZ0opbji3mX/47YsZSWV52T//in/+2T4GExmvQxPCM7LQTgghxIyO9sX52x/s5nBvnI++ZjOtsZDXIYkCiQZ9vPOatbxi8zLuf/Y4L/6HX/Kai5bz5itXcfnqejkbIKqKJMVCCCEm0FpzcijFE4f7+OGzJ3nyaD+3XLCMd1+3VtqvVai2WIjff/E5vOmKDL/a18OfbH2GtOXw4g3NvGhdE5tXxDi3tUZ6HYuKJkmxEEIIAIYSWS75+M8m/KwtFmTLxlYUih89d9KjyESxXXtOM6eGkvxyXw/3PX182suZhmLr+6/hqrWNRYxOiKWhdAVso6uU6gGOeh3HOM1Ar9dBzKCU4yvl2ABCWusL53rhAh+bpfC3kRhKM4ZerfUtc71i7riMM+kxKF9QNd7yR6uUYSonOWJpluj9wbbCmL7k0ty43PdS3bdSBma0wW9E6/1mqMavAhFT2xmn70efOZzu2p2Y5mpzPjbnMV6WwutvOqUcG0h8eVMelxWRFJcapdRTWusrvY5jOqUcXynHBt7GVwp/G4mhcmKo1mNZ7rv8lfJjKeXYQOKbjRSHCSGEEEKIqidJsRBCCCGEqHqSFC+Ne70OYBalHF8pxwbexlcKfxuJwVUJMVTrsSz3Xf5K+bGUcmwg8c1IaoqFEEIIIUTVk5liIYQQQghR9SQpFkIIIYQQVa8ikuJbbrlFA/IlX8X4mhc5NuWrSF/zIselfBXxa87kuJSvIn5NqSKS4t7eUu5DLaqZHJuiFMlxKUqRHJfCaxWRFAshhBBCCLEYkhQLIYQQQoiqJ0mxEEIIIYSoepIUCyGEEEKIqidJsRBCCCGEqHo+rwMQS2/b3m4+9cAeDvaMYjmgAJ+hOLe1hg/dsoktm1q9DnFKWmuGkxZ1Eb/XoZS1t93zGx47PDD239eua+Abt1/nYURCLL1te7u55+EOOgcS1AZ9aK3pjWfIWA5aa5RS+E3FxrYYt9+4vmTHQSFKxaGeUZ7vGuKqdY2014e9DmdJSFJcwfLJ8L7ToxOa8mkg62j2nBrh9778JOe11ZZUcpxPhgeTGWxHS1K8CJMTYoDHDg9wwycf5JGP3OxRVEIsnW17u/n0T/ayv3sUv6moDZqcGkqhtUZrUAbYDhgKTEOx99QQt39tBzVBUxJkIabxuV8e5N6HOzh/RYyPff8FPvnGi3nNxcu9DqvgJCmuUHc/uJ9/33aItOXMeDlHw55TI9z+tR18YMs53HHzxiJFeDatNcMpi6FEFsuZOW4xN5MT4ryuoTR3P7jf0+dbiEIanwxrrVGAdhS9o1lMwx3r3B+euY7WMJiw8JmK0bTNM8cGeO9XnmRDSw0fftX5khwLAXx7Rydfe/wo/++NF9EQCXC0L85fffd51rdEOX95zOvwCkpqiivQ3Q/u5zO/ODBrQjxe2nL4zC8OcPeD+5cwsqk5jmYomaWzP0nfaFoS4iL57IMHvA5BiEXbtrebV332Yd77lafYd3oEtMbRYGtwtEbjJsQaNwnWuaRYa7Ad9/dodwzM5837u0d571ee5JbP/Ipte7u9emhCeO7UUIq/++Ee7rx5Iw2RAABrmqK8+YqVfPR7L6D1tJvDlSVJiivMtr3d/Pu2QyzkONUa/n3boaK9CaSyNj0jaY71JyQZ9oADnnwIEqJQtu3t5mP37+JwbxxTMZYM59m5GWNHu8muUu4XuX9r3DIKy9Huf2uwHPfyplIc6U/wsft3lXxirLUmmbEZTGRIW7bX4YgK8k8/3cdLzmthdWNkws+3nNfKqeEUjx3q8yiypSFJcYX59E/2zmuGGNw3i7y05XDH1meW9E0gkbE4PpjkxGCSkVQWp8I+aZaSa9c1zPj7LzxyuEiRCFE42/Z287Z7H+f2r+2geyRFxnYwDIWRT3hzl3M0mIb734YCpc8kxeMvpwGfYuyDuaHAUIqsrekeTnH713bwtnsfL6nkWGtNPG1xejjFkb4EJ4eS9MczWLaMp6IwjvUl+NmeU7zu4hVn/c40FK+8YBlffLSy3kMkKa4g2/Z2s797dEKSOxfjh1AFxDPWksyOWLbD6eEUp4ZSpLMym1EM37j9uhmPh+GUVbRYhCiE/Oxw90gK23FwHHcBnWU7+Az3LU0DPsNNbjXQXh9iQ2sNjTUBogEftUGTmqCPmqCJ3zTwG6CUcuuOcWeJLUfjOBpHu1/dI6mSmDVOWza9o+4ZttPDKeJpq+JOYYvS8J+/OsTLNrURDU69/OyGc5t5vKOf3tF0kSNbOrLQrsDyB0cs5CfgK+5njnse7sBvKiytsSaNkUrBn75sw9jCqnzd8fixVAE+UxEwDfym4p6HOwq20GQ4lWUg7naTEMX1pzdv4F+kflhUiPw4Fwn4CPpMLEdjGm59sOlzk+F8DfHG1tkXzI1foGeo/KyyImM7uVlmNXZ/iYxV0HFxrmxHM5qyGElnyczzTKAQCzGUzPKDZ0/w6TddPO1lQn6Ty9c08INnT/Ce69cVMbqlI0lxgTnaHbyGk1miQR81QR+RgIlS852/nZ9te7t5+tgA2dxiEQMgV2MHExNiYOzf+Q4V+Xo7y9Y42uHUUIqhRGbRcY2mLQbiGbK2DOReuePmjZIUi4qQH+ccrQmYBtGAyWDScmeEDTXWXGIuyXDelk2tbNnUOiE5VgYo202OHTTRgI+OnlHSlk3XQJJte7uLkhhnLIfBZIZ42pbZYFFU33m6i4tX1Y0trpvOi9Y18sPnTlZMUizlE0soX+91rD9B72h6yRZA5E8nKuXO9JqG+8aglCLkN9jUVjNl6607bt7IPe+4gljI/WyktVsn5DfcWZKRtL3gU4WjaYvO/gTdwylJiEtA/jmeiiy2E+VgbJzDnc21bM1AMkt92IehFD5DcdnqBv7rXVfxkz99ybyT1i2bWnngT27kv951JZetasDvMzAMRUPYz2DSwnI0hlIoxZKXUaQtm+7hFF0DCUZTUh4him/r9k5esnH219CFK+rYe3KYgfjiJ9FKgSTFRWA7muFkluMDSY4PJhlOZQs6yH36J3vpHk6RsRyytttUyG+6yW1rbYgPv+r8aa+7ZVMrd992GQGfgc9UKDRp270drTWfemDPvGKJpy26BiQZLjXvu2H6T/H/9suDRYxEiPnbtrebO7Y+w4nBJOCOqeRKHYbTFq2xEPe84wq+8f5rFj2Du2VTK994/zXc844raK0N5eru3XoMR4N2NMcHE0uyIDlt2ZweTnF8IMloWur9hTd2nxhmIJHhghWz9yAO+AwubK/jV/t7ihDZ0pOkuMjSWZvekTRH+woze5xfXOdoTcBnjNXW2bmauo+//oJZ3yS2bGqlNuRDaU3WATT4c8u4D/SMzmngT2TcZPh0LjkXpWWmTToytpbZYlGy8jPE8YzlngXT7lkwcgvgtJ7bODdfWza18vHXX+D2NybXws29c3yGIpGxCzJjbDua4VSWk0NJjg8kiUsyLDz2nWe6uO6cJow5ln1uXhHjV/tLpzPLYkhS7BFHn5k9zrcmW8jscX7RSf5/ftMk4DPwG4rLVzfM+Y1iQ2sthmkQNA1CfhOfaaCUwm8Y3PNwx7TXy7dXOzUkyXApe+H40NgHnalIazZRqvJjXMhnAgrDUJhK4TMN1jRF5zXOzdeWTa1cvrqB1Y0RTMO9T0Mp0Iqg78yC5PnKWA5DiSwnBpMc7YvTO5ImmZGOPMJ7jqO5f+cJrjunec7Xuai9jkcO9FVEmY8kxSUgv4nFfGeP84tOMlmHjO2QtR20dsseso7D7Teun3MMt9+43i2ZyP0va9tuOYajefrYwFmzIcmMPZYMS3u10tY9nOJdX9xOdobOH9KaTZSi/Bh3rD+B7Wgs23H7mitNOlcuNp9xbiHyY2PKsgG3TZut3ViO9sWnHB+nksra9MczdPYn6BpI0BdPk5KxU5SYHccGiARMVk3arGMmy2IhAI70JZYqrKKRpLiEjJ897hpIMJTM4kyTyEy1uM52NFlHYxiKDS0185o92bKplY2tNW4rIsvBdtxFd2aubi9/mjCZsTkxmOTkUFKS4TLRUhvktqtWzXo5KaEQpWTywrrxZROWo4kEzCUpm5gsX0YRDfiwnYllFLMtvMtYDn2jaY71JTgxmGQwIZ14RGn74bMnuHpd47yuo5Ri0/Janjzcv0RRFY8kxSVqbDDNda6YXJqQP6XYVhtCA6Yy5ry4bjofumUTrbGQW35hKkzDfTtqiwUxFdz90AFODiVldqPMKKX44C2b+MBLz532MgZSQiFKS36MW1YXIr+qzlCgDEV7fYS7b7usaP2C8wuSV9SHAXfCILf2jrba0IQyirRlMxDPcHzwzOSGbGEvyoHjaB544RRXr22a93U3tNbyWEfvEkRVXJIUl7j87HHXQIJTQ6mxurPOgQRhv0ks7GdFXRifqcYWhCx09mRsYYl279dU0BYLEvSb+Ew1tvJblKd3XrOGP715w1k/NxX4fYq41DSKEpIf42pDflbUh/AZakkX1s1m8sI7n6lYURcmFvYT8hkc7YvT2Z/g+ECSgURGzqSJsrOza5CQ36S9ITzv6563rJYdRwcLH1SRyeYdZSSRsUhkLHyGQdhvcOD0CA4QMA1aaoOYuVnixbxZbNnUyqWr6jk1nCJrOfSOpsnaDoahWFk3/xeKKC2vu2QFn/vlQdL2mbIcW4Od2wLx7gf3z9ipQohCG0pmCfoMgj5jbJOjbXu7GU5mOTWUIugzaK4Jsr6lhkTGWvQYtxj5hXfdIyks26FnJMXxwQSGUqxsiEhphChrP3nhFFesrl/QdVfWh+kdTTMQz9AQnXnDj1ImM8Vl6DcHe+kdzWA5GrQmY9l09icYTmYXtejEdjR9o2necGk7w8ksp4fdgV8Bdq5R/vaO8q8ZqlZG7tX+O1evZqo+FAq466GDUlssimognuHEYJIjfQk6+xN87+nj/PX3XiAScPuvZWyHE0NJekZSRVlYN5OM5fDOa1YzEM/QNZAkmx8fHc1AIiPjoyhbWmt++sIprlg7v3riPMNQnNtaw86uwcIGVmSSFJehrU92UhP00RYL4TMNt6bYVNSFfFy5rhEr14lirl0sLNsZWxU9lMxy9fpGGiIBTEPlThMaLKsLURP0sfXJziV9bGLptNQEaYwGeNf163j3tWvO+n1+8xapLRZe0FqTtR2+9JsjKAWxUIC2OrdswnY0o2mLD77yPK5e3+hu3lGkeBIZi77R9FjXiE3LY9SH/ZjmmfGxLSbjoyhvh3pGSWQs1jdHF3wb65ujPHtssHBBeUDKJ8rQyeEksZAPhaImmNuiGc1IyqJ7ODXhsqahiAR8hPwGpuF2lrAc7c5uaE0660x5yi+esVjTFEGNm1PUaE4NS11xuVJKUR8JEA6YvPfG9Xz58aMoBfmnP2trTMPdoltrPXYqW4hiyo9vANGAj2jANza+bV4R49SQO8b5TSO3KDi3yNg0CJhn5nmMGfpy59mOxnKc3PdcuzVHk7U1yaw9bd/VeNZmTaOMj6Jy/Hz3aS5f07CocX9tc5SdnYOFC8oDkhSXme0d/YymLHpG0gR9Bo3RANGAj1TWYVns7Jpf29GMpLKMpKa4sRksj4Xpi6cJ+01G0xYDiQwZyyHkN9ne0c/V6xd2ikV4L+gzaa8PEwmYpLI2flPltgc/kyD//Y928+FXnY/PlJNJoriifpOj/QkcR+M3jbGzVpPHt6w99Qf6PNPIbWk01rZSoXUuEbbd3fCcBW42MH58jGcs+uMZ0pZDWMZHUaZ+8sIpXn3R8kXdxvrmKF97/GiBIvKGvOOVke0d/dz10AHCfgNDuW8Kp4dSDCTSWI6eUy/aubrtqlVYjqY/nqZ7JDVWOxcJGNz10AGpnStzSine/+L1OJqxhHi8/3rkCP/vR3uk/Z4oqu0d/Qwks9i2ezbLsh1OD6cYTVvzHt/ys8BZ2yGZsRlNWcTTFqmsjeU4C06I4cz4OJBIc3rIHR8NBWG/jI+i/PSOpjnYM8r5y2OLup3mmiAZy33NlquSTYqVUrcopfYppQ4qpT7sdTylYOuTnfgMRWM0SGttCH+unjiRcbjzpg0FnZ24en0jd960gWTWwdHuqcq2uhANkSA+Q0ntXAW44+aN/MnLzm7RBm7Lqa8/cZSTQymGU9niBiaqVn69xLK6ceslDEVDJFBSs6/58TGRcdC442NrbYjGqIyPovw8tKebi1fW41/kmUGlFOtbath9YrhAkRVfSSbFSikT+HfgVcBm4G1Kqc3eRuW9k8NJQn73KasJ+ljVEGF9S5TakG9J3jCuXt9ITcjHOS1RVjVEiAbcapuQ31jy2rms7TCYcBvgv3B8CEtaHS2JO27eiGkogj5FyGcQMM9USaZtzfeeOU7vSJru4VRF7GsvSlt+jIsGcuNbcw1rmiIkMqW3DXl+fFyfGx/z6zuKMT4KUUg/3X2Ky1bVF+S2VjWE2XViqCC35YVSrSm+Gjiote4AUEptBW4FdnsalcfG17HlTVdL7NV9WrZDPGOTyFjE0zbxjHvKMpGxiafzP7dyl7Fzvztz2UTu++RT+k/81ctoy+2vLgorGjBJZm18hsIA/CZkcn//u35xgETG4m1XryZtObTUBgmNOxaEKCQvxrjFKLd4hZgslbV5vKOPt15ZmPLL1U1RXijjmeJSTYrbgfHnn7qAF42/gFLq/cD7AVavXl28yDx02ao6vrb9GLajCZiK2pAPn2kWpJbYdrSbnOYT1VxyumlZLT9+YZS+0TQZ23EXYilQCv78W8+OXS6f+KatpZnRHUlZtC2u3Kloyu3YfN8N67jroYPuKnxbk38GFW4Zxed/fZjRtMX7bljHicEkdWE/jdGAdKcoM+VwXObHOMfRBHwG0YCJ31eYMa6QtNY4Gt50eTv/uu0giUyW0ZRFxgbTgKvXNHC0L46V62aRX9xna41lOxN/nvty//tMJwzLHn8Z58zPtMa2z1wn3z0jfz3TULzr2rW8fHOb13+mOSmH47KS/eZQL+uaotSG/AW5vTWNEX7w7ImC3JYXSjUpnurddsLUodb6XuBegCuvvLLiz+tu7+jnJ7tPUxfyEc/YZCyHwaTF269azub2GN3DqTMJbW6mdjQ3M5tI24yOS1wTaWvCZeMZi1R2HsmshmP9SY71z+0UYdBnEA36iARMogEfkaD7PRo8+78j438eMIkEfVywoo7aYKkeqmcrt2Mzv4Pdv/3y4FhCbOB+8MlP2H9jeyfxtM0dLzuXoWSWZNamLRZadA2aKJ5SOy5tR3OsP8HR3jhZ2+HZziHuf+4kkYBBIm2TyjpkbIeXbKgnadn8Yk93LiF0xhLJ8cnkmaRzcsLpTJGY5q4zOTGdInF1k1HnrGR0uj+g5cB9O09w307vEoOXnufNjn8LUWrHZbX52a7TXFKg0gmA5fUhTg2nSGXtsjyrWKqZRhcwfmpgJVC+Hz2moLXbB3Ny2cDZJQbu7x491Ec61wnAyfWQtWzNlx4/xpceP7YkMfpNtw9yPOP26/QZbtcL01A4WhMN+Hj9pSuIBn1EA5MS2rFE14c5h36hM6kLF+YTrJjeHTdv5AuPHEZj48ttfafd/lXuseZo7n/2BImMu4ECwInBJC21QSKBUh1GRCkbSWV56T9tm/EytgMP7evhoX09xQmqCAzlbvhhKoXPVG7ZkuF+9xluP3mf4baQ85lq7HKmUpimkbuc+/v8lz93e+4mTn4ubK/z+mGKMuA4ml/s6eYjr9pUsNv0GQbt9WEOnB7lopXldxyW6rvZk8AGpdQ64DhwG/A73oY0u5/tOsXeU8P0xzO55HZczey4Gtp8ErxUH4l9hpowM3vWDOykmdlIwKQmeGZmNhowiQZ9Y7OAb/v842ObheTlm+m/7Wo53VUp4hkb37iJX6UUpuG2bHvj5e185+njPLinm0TG5mOv3UzAZ3BqKEV9JEBDxC/lFGJe5tIDO39E1YZ8YwmgzzDG+g6PTw7df+eSxrEkMnf5/O/HJZn5BHRCEpq/jQmXc3+W//3E21b83/t3URMwUcrti+wGrYmnbe5955XufY4lvWrJXydtsRDRMjqzJrzz/PEhQn6D5fWFrYFf1RBm76lhSYoLRWttKaX+CPgpYAJf1Frv8jisWd37cAdPHR1Y8PUNRS45PTtxfbZzkIzlEPDlZmuVIuto6sN+/vKW8yYkuwFfYU9py2KS6pBfcDd+Yt/R7s8/sOUcagI+vvL4UX5zqI+PfPd5/u7WCwkHTAYTGeJpi9ZYkKCv/E6XCW9E/CaPf+RlnBhMohR87Hu76E+kieTGGaUUyaxNUzTIv7z1Eo+jnd7qhih98TShccd+MmvTXh+hLiJnuUTp+tmuU1y2uqHgt9teH2bvqZGC324xlGRSDKC1/jHwY6/jmI+1zVEGk1lCPmPCjGsk9z1fYhAJmtTkvkcCvrFZ2qDPmHIWYXtHP0d6RjnSnyBtKZprApiGgXI0v//i9Wxsq13Sx3XbVau466EDJLM2tuPQG89g2e5uU7J7U+WYbsFdxrL52mNHeff1a4kGTf7jVx08c2yQv/j2s3zyDRcRC/vJ2g4nBlPUhf0yayzmxDAUy+pCpLI2jtZcuaa+LBbZTZYfHwcSaUZSFhnbXex286byqesV1emnu07zzmvXFPx2VzZE+M2h3oLfbjGUbFJcjv7pzZfQPZJiNFW4npr5Xex8hmJZLEhvPMOp4TRrm6J8YMv6oiSkV69v5E42cO/Dhzg+mB6LJWs73PXQAe6ksBuHCG9MteDOVKCV4su5rTvfed1aIgEf//Lz/ew5OcKfffNZ/uFNF9MYDaC1Hps1bq4JEg7IrLGYm/xC4vqwj5GURdpyF7q94+oVJT+2XL2+kVtOtY0l9MFcQv+T3ac5b1ms5OMX1eloX5zeeJpzW2oKftvtDWEOdI8W/HaLQZaOl7j8LnZhv0lN0M/axijt9WFiIX9RB9ur1zcSCwdorw+ztilKTdBP2G/K7k0V5o6bNxLym/hN95gL+MyxBZbf2tEFwGsuXs5HX3s+pqHo6I1z59adnBq3rWfWdjg5lKRvNI3jyGJyMbv8ONcQCbK6Mcq5LTUsi4V4prM8NgF4pnOIZbEQ57TUsKohIjvbiZL3012nuHJNA8YiF8JPpaU2yHAqy0gZ7oYqSXGJG7+LXZ5XOyaVUixi6cQzE+uKwe04ksh1PwHYcl4rn7j1AgI+g+ODSf5k606O9ScmXGcomaVrICnbRItZlfvYUu7xi+rzwPOnuGJN4euJAQylWNUQKcvZYkmKS9zyWPisHsJzXeRmKEXAZxDwGYT8ubrm3Fcs7Kc+EiAW9lMTdOuaa0N+okEfQb+JMUVN6GJiEeUjGjCZPMHraHcRaHDcYstr1jfx6d++iEjApHskzZ9s3cnBSYOg5Tj0jqQ5PpiUrbrFtAoxtuTHu0jAR03IHc/y3xujAVpqg7TFQiyrc7/aYme+WmqDNOe+mmqCNEYD1IX91Ib8uTMnU6/3KGT8QhRL93CKA92jXLBi6bpDrKgPc/B0+SXFUlNc4sYvcgv5DVJZt9Yuv/gk354o7DcJ+c2xFkOGYtGLnVJZe6yhvaM177l+LZ/+yV5Slk3Qd3YsojKMX3BnKDchdrT786ZogBODZ2a/LllZzz+/+RI+dN9zDCaz/Ok3d/LJN1x0Vp/UdNbm+GCSxmigYDsnicox2zg3mVJqbHFy0OeOe4XuujOV/E5243eSs2zNu69byz/8zB0bQz63i4uMjaJU/Wz3aS5bXb+kmy8ti4XKcqZYkuIStr2jn61PdpLMWGRtd0X2uqYo775+LTdvbiPkM5ekHihv8m40t17WTl3Yzz0Pd7D/1BAZW+P3GfzvU27dXKFrnPOPvzeeZlVDhNtvXM8WWdG95PIL7r7wyGHiGRtTgU/BXQ8d5AuPHOa2q1Zx27j+1Octq+Uzb72UD377OfriGT747ef421sv4Kq1E48H29H0jKQZzS3EK9fd8Lbt7eaehzvoHEjIcVlAYb9J54BbgrOqPswHtpw7Nqb4TfeMVzB/5muJx77pKKUwcxsYBcadaP2ty9upj/j59E/20tEbR2vNuqYIjdEAbbHQmV3xHD1hpzytC1dznx8vu0dTrGmMynEppvWDZ09w3TnNS3of7fVhdhxbeItar5Tnu1IVyHed6E+kaYuFaK0NUhP0ccfLNvD6S9uJBHyevCls2dTK7TeuJxoK0BoLsaIuzHAqy79vO8ju48MTTq8vRv7x98XT1If9dI+k+Nj9u9i2t7sgty9mdsfNG3nub17JnTedi63BAXyG23/1C48c5quPHZlw+XXNUe667VKW14VIWQ5//b0X+PWBqVvyJDM2XQNJBhOZgiYFxbBtbzcfu38X3SMpOS4L5ImOPu566ABZ22FtU4RlsRBJyyHoN2ipDbK6McKqxghtsRD1kYBnY99cxDM2KxvCbGyrxdLwdz/ew5OH+6kL+2mqCbpjZn2YVY0R1jZFWFEfpjEaIOQ3F3Vmb/x4WReS41JMr280zQvHh7i0gFs7T2VFQ5hDPeU3UyxJcYn65o5OQn6DunCAgM+kJuQn4DO45+EOr0Pjnoc78JuKSMCXO43pbhjy9e3HaK8Ps6I+TFM0OPb7hRjfdSN/H35TlcTjryZfeOSwuy2tYWAoY2wXsG/nOlGMt6I+zGffeilrGiNkbc3f/mAXP911asrb1VrTH8/QNZBkNF24FoZLbapjX47LxflG7rUeCfjwGQaxsJ+w3+CbT3VRG/LPaee7UjDfY0MpRchvUh8JsKI+PJYkN0WDRIO+Kdd1TEfGSzFXD7xwiktX1y95uVFbLEj3cJrUuAXa5aA8RpsqoZS7PfPKhgjdI2migYnVLWG/SddAYpprF0/nQGLC7nYwMbaQ36Qu4mdZXYi1TRGW17mzIZHA3Kt1plrNXSqPv5pM1YnCUJDIOlOeFWipDfLZt17KxrYaHA2f/sk+vvvM8WlvP2s7dA+nODmUJFsGC/FmO/bF/HWPpIiF3A/WPtPAyCV15fY3XeyxkU+S6yJ+2mIh1jZHaW8I01wbpDbkn7HcSMZLMVff33mcF61tWvL78RkGbXVBjvaV1zEoSXGJiAR8tNeHaYuFCPgMVjVESE76hJXM2qxsiHgU4RnziU0pRTjgzoYsqwuxssGts5utzGKq1dyl8viryXSdKKIBk+aawJTXqYv4+ec3X8LFuX3v//Whg3zt8aMzlkokMzbHB5Il39eylF+X5WpNY5SUVf6v9aU4NoI+k1jIT0ttkFWNEdY1R1nZ4E40NNW4JXWGUjJeijk5NZRi76kRLlni0om8FXVhOsqshEKSYg+ZhqIm5BtrETT+dMbtN64na2sSGQut3e9ZW3P7jes9jHjxsQV8BvURdxOQVY0RGiKBKWdAbrtqFZajSWbtknv81eR9N6zD0W5rNUc7ue/uz4M+k1h46k4S0aCPT7/xIq5e5y6U+uKjR7jn4Y4ZE2NHuwvxSnnWuJRfl+WqUv6mxXgcKtd2LhwwqQv7aY2FWNMU4Q+3rMfRkLJsKOO/oVhaP3j2OFetbShKpxZwO1AclKRYzMZvGjTVuAtIWmtDU5YVbNnUysdffwGttSGGkllaa0N8/PUXlMRq4kLF5jcNGqIBVjVGWFYXmrAt8NXrG7nzpg00RYMl9/iryR03b+TOm84l7DexHPeU7J03nTvWoaIxEsBnTD2MBP0mn7j1Al56XgsA33yqi888eAB7ll3u8gvx+uOZktsRr5Rfl+WqUv6mXj0OpRSvvHA5f/9bF9JeH2EkbZXt31AsrfuePs6165e268R4y+pCHCizXsXSkq2I/KZBfcQ/a5/WyS2fPnHrhSU3uG3Z1MqWTa1jsf71919g1cMLb08VCfiIBHzYjmY0ZTGSznL1+kauXt/I+iXYm13M3R03bxxLggHufnA/F//NT4lnbKIBk3dds4Y3XrFyyuv6TYO/evX5hAMmP37+FD987iTxtMVHXrVpxgVUWmsGExlGUlnqI+5GCqUif+yLwiutj0Dz43WrPjkuxUz2nRqhbzTN5uWxot3nivowjx6cugtRqZKZ4iLwGQbNtUFWNoTnlBCXS8unpYjVNBR1ET8rG9yV2PNZnCeW3t0P7ueuhw6SzNpjLdr+4+EOvvHE0WmvYxqKP3/5Rt6cS5x/ua+Hj92/i/QcViXbjqZv1N0RL2OVZkmFWJxyGvOmUwmPQVS2bz7VyfXnNhe1neGyuhBH+hJl1XpTkuIl5DcNmqJBVjWGiYX8c2pPVk4tn5Y61pDfZFldqCC3JQpjqhZthnJbQs10fCul+IOXrOfd160B4PGOfj78neeJz7EdW35HvFIsqRCLU05j3nQq4TGIypW1Hb77dBc3bmgp6v3GQn4MBX3xTFHvdzEkKV4C0aCP5XXuQrK6yNyS4bxyavlUTrGKwpipRdtsJQ5KKd517Vr+6KXnAPBs1xB/8e3nGErOreNEvqSiqwy6VIi5q4RxpBIeg6hcD+4+zfL6MMvrw0W/7xX1YTp64kW/34WSpLjAmqJB2mITF43NRzm1fCqnWEVhzNSirT7sn3bR3XhvvHwlf/nK8zCUW+f2p/+7k97R9JxjsByHnhG3pCJtlVdjeHG2ShhHKuExiMr11ceP8pKNxZ0lzltWF+Jwb/kstpOkuMDMRdbrlFN7onKKVRTGTC3aDEPREJ3bgrhXXbiMj712Mz5DcaQvwZ1bd3JyKDmvWNJZt7dx72haSirKWCWMI5XwGERlOtwbZ/eJYV60buk37JhKW21IZorFwpVTe6JyilUUxmwt2mpD/lk3Zsm7cWMLf/+GCwn6DE4Opbhj606O9s1/8BxOZukaSM65PlmUlkoYRyrhMYjK9OXfHOHGjS1F60082bK6EIfKqFexKqdVgdO58sor9VNPPeV1GIvmdUufxSr3+OdoXqcCKuXYnM3dD+7nC48cJp6xifgN3nT5St553do5Xff5riH+6rvPE8/YxEI+/uFNF7OxrXZBcUSDPhqjU28IU+Eq4rgs1zGkXOMukjkfm6V6XJar0bTFdZ/8BX//hotorgl6EsPRvjif/3UHv/jzLZ7c/wymPC6r7p2jVJV7S59yj18s3OQ2bSnL4cuPH+Wrvzkyp+tftLKOf37LJdSF/QynLP78m8/yXNfggmKJpy26BpIMxDNl1QZIlO8YUq5xi8r3jSeOcmF7nWcJMUBbLETXQLJsStwkKS4R5d7Sp9zjFws3XZu2b+3omvNtbGyr5a63XkpzTYB4xuZD9z3P9sP9C4pHa81ArktFag69kEVpKNcxpFzjFpUtYzl84ZHDvPqi5Z7GEfKb1IZ8nJjnmhGvSFJcIsq9pU+5xy8Wbqo2baahSMwzIV3dFOHu2y5jRX2ItOXw1997gW37ehYcV9Z2ODGYpHskNevW0sJ75TqGlGvcorJ995kulteFOacEdoRdXhfmSG95vB4kKS4R5d7Sp9zjFws3XZu2mqBvzovu8pbVhbjrrZeyrjmK5Wj+7ke7eeD5k4uKbzRl0TWQYFh6G5e0ch1DyjVuUbmytsPdvzjI6y9Z4XUoALTFghxewCJqL0hSXCLKvaVPuccvFm6mNm3NNYF5315TTZDPvOUSNi2rxdHwjz/bz7fnUYoxFdvR9I6kOSHbRZesch1DyjVuUbnu29FFU02A85fHvA4FgNbaEIfLpAOFJMUlotxb+pR7/GLhZmrTFvSZs+50N5VY2M8/vfliLl1VD8Dnth3iS785sujFc6lx20XLQrzSUq5jSLnGLSpTKmvzmZ/v502Xr/Q6lDFuW7bymCn2eR1AtaukVj5bNrWyZVPr2GP66++/wKqHy/sxibm54+aNY72Kx7v7wf18/tcdxDM2Yb/JW66Ye6u2SMDHp954EX/7g9081tHHVx47SiJj8YcvOWdeW6dPlt8uOp62aKoJEAnIMFgq8mMIMHEcKcGxsZLGblE5vvDrDta1RNmwwLaWS2FZLMThXkmKq8pCBsh8Kx+/qSa08vk4lO3gWomPSSxMvlWb25kC0pbNlx8/CjDnxDjgM/jb12/mUz/Zx0N7u/n2juPE0zZ/9vKNC949cntHP1uf7OTkcJLlsTDvvm4Nr71kBb7q623sqZnGzFIfR0o9PlGduodTfP7Xh/mb113gdSgTtMVCnBpKYdlOyY+zpR1dmVhon8pKbOVTiY9JLMz4Vm2mYY61avvmPOuDfabBR161iddd7LYWeuCFU/zdj/aQtedfG7y9o5+7HjpAXzxNLOSjL57m0z/dx307uhhKykK8YpltzCz1caTU4xPV6eM/3M1Lz2thWV3I61AmCPgM6iN+TgymvA5lViWXFCul/kYpdVwptTP39WqvY5rNQgfISmzlU4mPSSzMVK3alOKslfpzYRqKP7l5A7ddtQqAX+3v4aPfe2HefYi3PtmJz1CE/SYK97vPUHxjeyd9o2mODyZJW9LbeKnNNmaW+jhS6vGJ6vPIgV6ePNLPrZe2ex3KlJbXhcqiA0XJJcU5n9FaX5r7+rHXwcxmoQNkJbbyqcTHJBZmcqs2pRRac9ZrZa6UUrz/xvW874Z1AGw/MsCH7nue0bQ159s4OZwk5J847IX8BqeG3cby6azN8YEkfaPpstmBqRzNNmaW+jhS6vGJ6hJPW3zovuf43WvXElrg+LrU2mIhjpRBXXGpJsVlZaEDZCW28qnExyQWZrpWbfnZ3oX6nRet5o6bzgXg+eND/Pk3n2UwkZnTdZfHwqSyE8suUlmHZbHwhJ8NJbN0DSSJzyPhFnM325hZ6uNIqccnqsvf/Wg3G9pquGx1g9ehTKulNsjh3tJvy1aqSfEfKaWeU0p9USk15bOslHq/UuoppdRTPT0L3/WqEBY6QFZiK59KfEzzVUrHppematX2Jy/bwJ+94rxF3/ZvXdbOh1+1CUPBge5R/vR/n6VnJD3r9W67ahWWo0lmbTTud8vRUybqluNwejjFqaHUguqXS00pHZezjZmlPo6UenzlpJSOy3L04O7T/GJPN++8Zo3XocyoXNqyKS96dSqlHgSWTfGr/wM8DvQCGvgEsFxr/Xsz3d6VV16pn3rqqYLHOR/5ldRdAwlWztB9ohrb+FTYY55Xy4NSODZL0amhFImMxVd/c4Rv7ugimZ1/yzZw6+g+8aPdZG3NsliIf3zzxbTXh2e8Tr77xKnhJMtiYW67ahVXr2+c8TpKKRoifurC/kW1g1tCZXdcznXMnO36SzWuVNi45aU5H5ulcFyWk+ODSV73r4/wxzedy6ZlpbFRx3SODyS5+6EDPPzBl3odSt6Ux6UnSfFcKaXWAj/UWl840+XK5YU0vo1P2G+SzNpkbV3RMwwV+JjLLvkoRVnb4f/9aDdffuwohnIX4Gntbg/9u9esmVdivOPogLvoznJoigb4hzddzLrm6JLE7TcNWmqDpVi3V1XH5VKPKxU4bnlJkuIlkLZs3vQfj3FRe4zXXVKai+vGy1gOv/+Vp9j98VeWSlu2KY/LkohsPKXU8nH/+QbgBa9iKbRqbONTjY9ZzM5vGnxrRxeGAtMwMJSR+z7/lm1XrGngH998MTVBH33xDH/6vzvZe2p4SeLO2g4nBpMMJaR9m5eWelyRcUuUMq01f/Wd54kGTV578Qqvw5mTgM+gIern+GDS61BmNKfNO5RSG4G/BNaMv47W+qYliOkflFKX4pZPHAFuX4L7KIrJp98OdI+wLDaxf2Clt/HpHEhQP2mb37Df5MDpYd527+NyarKKJTI2vkkfy+fasm2qsovPvOUSPnjfcwwksvz5N5/j799w4dg20YWWqYAa43I23bgyfiydrfxhpt/P5faF8Mq///Igzxwb5KOv3Vyq5VxTWl4X5nBvnDVNS3MmrxDmOlP8LeBp4K9xk+P8V8Fprd+ptb5Ia32x1vr1WuuTS3E/S22q5vQjKYu++MTFQJXexmeqVea9o2lG0va8NzsRlaUm6GNy17O5tGz76m+O8OXHj5K2bMxxO+X95mAvn33rpbTWBklmbT78ned5vKNvCR+B8Mps3Stm2xxktt9LyzVRqr71VCdfeewof/6K80qxjGtGbbXBkm/LNtek2NJa/4fWervWekf+a0kjK3NTnX5riPjpj2erqo3PVKvMBxJZGqN+OTVZ5d53wzo0CjvXss3OtWx7yxUrZ7zeN2cou1jVGOGu2y5lZUOYjOXw0e/v4iH5sFVxZuteMVv5w2y/l5ZrohT96LmTfPKBvXzwlk00RgNehzNvLbUhDpdzUqyUalRKNQI/UEr9f0qp5fmf5X4upjFVc/rmmiC1QbOq2vhM1bqoNuSjKRqccDk5NVl9xrdssx0I+sw5LbJLZm0mnzEcX3bRFgvx2bdeyvqWKLaj+fsf7eGHz5XlCScxjdlaos22Ochsv5eWa6LU3P/sCf76e8/zwVeeN2uHnVK1rC5ER4knxbPVFO/Are3NvwWNL5nQgHxsnsaqhgjdIykigTN/4mTWZkNbjG+8/xoPIyu+LZtaJ7yZvO3ex6f828ipyepzx80buePmjQwmMvTH57YBR9hvulsxj0uMJ5ddNEYDfOYtl/CR7zzP7pMj/MvP9xNPW7x1kRuHiNIxeVwZb7rxNz/GzPb72W5fiGL66mNH+OyDB/jwq85ndWP5vk8uqyv9Xe1mnCnWWq/TWq8Hzs/9e+wL2FycEMuTnH6bnvxtxGR1YT/+ObbpecsVK3E0s5Zd1Ib8/OObLuGK1fWAe8r8vx45TCm3oRSFMdsYI2OQKAeW7fC3P9jFf/6qg4++dnNZJ8Tg1hSfHk6X9GZIc60p/s0cfyZy5PTb9ORvIyZTStFUM7cauXdet5bfvWYNQd/sZRfhgMnfv+Eirj+3CYCvP3GMf/vlIRxJjCvabGOMjEGi1J0cSvK2zz/OM8cG+ZvXXUDbpM5V5chnGjTWBOgaKN22bDOWTyillgHtQFgpdRlnTljGgPL+yFIEcvptevK3EZNFAj6iQR/xtDXrZd953do5b/AR8Bn8zesu4B9+uo+f7z7Nd585TiJj8RevOA/TKJ92RmJ+ZhtjZAwSpchxNN/a0cmnHtjLKzYv4/WXrMCooHFqea6EYqk2WFqs2WqKXwm8G1gJ/EvuZxoYBf5q6cISQlSjxmiARMYueImDaSg+dMt5RPwm33/2BD/ddZp42uavX3M+gcnNkoUQosi01vzmUB+femAPacvhL1+5qWQTx8Voqw3S0RunZDZ7nmTGpFhr/WXgy0qp39Za3weglPqK1vpdRYlOCFFV/KZBfdjPQGJui+7mw1CKO152LjUhH19/4hiPHOzl/3zvBT5+6wWz9kYWQoilEE9b/Pj5k3zpN0cYTGb5rUvbue6cJowy2pRjPtpiITp6Rr0OY1qzlU/cP+7fv5v7501KqXoArfXrly40IUQ1qo/4GU1bS7IYQynFe29YRyRg8vlfH2bH0QE++O3n+OQbLqImNKcNPoUQYlFGUll+ua+HHz93kkcO9rJpWS2vunA5l62ur9hkOG9ZXYiH9/d6Hca0ZnsXWAXsAr7AmdZsVwH/vMRxCSGqVH7R3amh1JLdx9uuBW6QhgAAGdpJREFUXk1N0MdnHzzArhPD/Nk3n+XTb7qIhkj5NcQXQpS+jOXw892nue/pLp7o6GPT8hiXra7nn99yCbGQf/YbqBDL68Ic6SvdtmyzJcVXAHcC/wf4S631TqVUUmv9q6UPTQhRreaz6G6hXnfJCiIBk08+sJeDPaP8ydad/OObLqa1AlZ5CyFKQ9Z2+PrjR/nctkO01ga5YUMLb3/R6gk9sqtJc02QvtEMqaxdkttUz1ZT7ACfUUp9K/f99GzXEUKIQmhaokV3473s/DbCAZO//cFuOgeS3LF1J//05otlIxkhxKLtPjHMHVufIRr0cefLNrC+pcbrkDxnGoq2WJBj/Qk2ttV6Hc5Z5rTsWmvdpbV+M/AA8LWlDUkIIdyelg2RpT+teN05zXzyjRcR8ht0j6S5c+vOkl4IIoQofT987gRv+/zjvGJzGx965XmSEI+zvC5ER09pllDMqxeR1vpHWmtpxSaEKIr57HS3GJevbuCf33wJtSEfA4ksf/rNZ9l9YnjJ71cIUXm+/VQnf3P/Lj7yqk28eEMLqsIXz81XayzE4RLd7lkadAohStZ8drpbrPOXx/jMWy6hMRpgJGXxF99+lqePDhTlvoUQleGXe7v5fw/s5cOvOp81TZXXZ7gQltWFONA94nUYU5KkWAhR0vKL7ophfUsNd731UtpiQVJZh49893kePVi67YOEEKXjSG+cP/vmTu582Qba68Neh1OylteFK6N8QgghvNAYDRTtFGR7Q5i7b7uMVQ1hsrbm/96/iwf3nC7KfQshypNlO/zRN57m1kvbS3IBWSlZXhfiaIm2ZZOkWAhR8vxFWnSX11Ib5LO3Xcq5rTU4Gj75473c/+yJot2/EKK83PtwB6ZSvGJzm9ehlLz6sJ+05TC4BDuXLpYkxUKIslCsRXd5DZEA//LmS7hwRQwNfPbBA/zPE8eKdv9CiPLQNZDgP391iPdcv04W1c2BUor2+jCHSrCEQpJiIURZUErRXBMs6n3WhHx8+k0Xc+WaBgC+8MhhPv/rjiXtnSyEKC+f/PEeXrG5jTbZ+GfO3LZspdf6UpJiIUTZCAdMaoq06G7sPv0mf/dbF3LjhmYAvrG9k0//ZB+OI4mxENXu+a4hHu/o5zUXr/A6lLKyrC7EIUmKhRBicRqjAYwin6IM+Aw++trNvPICt17wvqe7+LNv7iRrO0WNQwhRWv7xp/t4/aUrSnLL4lK2vC7MgW5JioUQYlF8pkF9ERfd5ZmG4i9feR5vvKwdgNG0VfQYhBCl4/muIfacHOKl57V6HUrZWVEf5lAJJsXFPQ8phBAFUBf2M5Kyij5TayjFB156Dhe21/E7L1pd1IV/QojS8m+/PMCrLlou48ACLIuFODGYIms7JfX3K51IhBBijrxYdDf+vl93iZwuFaKaHetL8FhHn8wSL1DAZ9BcE+BYf8LrUCaQpFgIUZa8WHQnhBAAX3z0MFs2tsqH40VobwhzsMRKKCQpFkKULS8W3Qkhqls8bfGdp7t4uWzUsSjLYiFJioUQolC8WnQnhKhe3995gvOXxzwr4aoUK+rD7D814nUYE0hSLIQoa8Xe6U4IUd2++vgRtkgt8aKtbAizv1uSYpRSb1ZK7VJKOUqpKyf97iNKqYNKqX1KqVd6EZ8Qonx4uehOCFFdXjg+RH88w8Ur67wOpeytqA9zpDdRUhsheTW98gLwRuDh8T9USm0GbgMuAG4BPqeUkip2IcSMwgGTqCy6E0IssW9sP8aNG1pkLUMBRAI+aoI+jg8mvQ5ljCdJsdZ6j9Z63xS/uhXYqrVOa60PAweBq4sbnRCiHDVFAyh5oxJCLJFU1uYHz57gxRtavA6lYqxqDLP/dOmUUJRaIV470Dnuv7tyPzuLUur9SqmnlFJP9fT0FCU4IeZCjk1v+EyDBll0Ny05LkUpKqfj8hd7ulnXHKWlVsq1CmVFfZj9p0unA8WSJcVKqQeVUi9M8XXrTFeb4mdTFptore/VWl+ptb6ypUU+tYnSIcemd2TR3fTkuBSlqJyOy2891cl15zR7HUZFaa8Ps+fksNdhjFmyIjyt9c0LuFoXsGrcf68EThQmIiFEpVNK0VQT4NRQyutQhBAVpG80zVNHB/jd69Z6HUpFWdUY4Zf7ur0OY0ypTancD9ymlAoqpdYBG4DtHsckhCgjkYBPFt0JIQrqh8+d5LLV9bKDXYG114c52pfAsh2vQwG8a8n2BqVUF3At8COl1E8BtNa7gG8Cu4GfAB/QWttexCiEKF+NsuhOCFFA9z3dxbXrm7wOo+KE/CZNNQEO98a9DgXwrvvEd7XWK7XWQa11m9b6leN+9/da63O01udprR/wIj4hRHnzmwb1YVl0J4RYvGN9CY71JbhIehMviTWNUfaWyM52pVY+IYQQBVEfkUV3QojFu//Z47xoXSM+Q8aTpbCiPsTuE6Wx2E6eYSFERVJK0RgNeB2GEKLMfe+ZE1wjpRNLZk1jlF0l0oFCkmIhRMWKBn1EArLoTgixMPtOjTCUzLBxWa3XoVSsNU0R9kpSLIQQS08W3QkhFuoHz57gReubZFvnJdRSGySesegbTXsdiiTFQojKFvAZ1MmiOyHEPGmtuf9ZKZ1Yakop1jVF2V0Cs8WSFAshKl5DxC+LZIQQ87LrxDBZ22F9c9TrUCremqYou0pgsZ28SwghKl5+pzshhJir+589wYvWNUn5VRGsaYqws3PQ6zAkKRZCVAdZdCeEmCutNT949gTXrG/0OpSqsLYpyq7jQ16HIUmxEKJ6NNXIojshxOye6RzEZyhWN0a8DqUqtNeH6R3NMJTMehqHJMVCiKohO90JIebi/p3uhh3yIbo4DEOxviXK813ezhZLUiyEqCqy050QYia2o/nhcye55pxmr0OpKuuao+zsHPA0BnlnEEJUFaUUzTVBr8MQQpSo7Yf7iYX9tNeHvQ6lqqxvruHpY4OexiBJsRCi6oQDJjVBWXQnhDjb93Ye55p10pu42Da01bCzcxCttWcxSFIshKhKjdGA7FIlhJggYzn85IVTXHuOJMXF1hQNoICugaRnMUhSLISoSj7ToCEivYuFEGf8an8PKxvCUmLlAaUUG9pq2XHUu7piSYqFEFWrLuIn4JNhUAjh+s7TXbKts4c2tNbwxOE+z+5f3g2EEFVNZoSEEADDqSy/PtAr9cQeOm9ZLU8elpliIYTwRMhvUhuS3sVCVLsHnj/Jhe0xakKyCNcra5uinBhKMhDPeHL/khQLIapeYzSAaciiOyGq2f8+2cn10pvYU6ah2LSslicO93ty/5IUCyGqnmkoGqOy6E6IanW0L05HT5xLV9V7HUrV27Qsxm8O9Xpy35IUCyEEUBvyE/KbXochhPDAN5/s5PoNzfhkt0vPbV4R45EDkhQLIYSnmmoCKOldLERVsWyHb+3o4iUbWrwORQDrmqL0jqY5OVT8fsWSFAshRE7QZxKTRTZCVJVf7uuhMRpgVWPE61AEYBiKi9rr+PX+4s8WS1IshBDjNEQC+AwZGoWoFl997AhbzpNZ4lJyYXsdD+45XfT7lZFfCCHGMQxFU40suhOiGhzrS/Bs1xDXrpeuE6Xk0lX1PHqol4zlFPV+JSkWQohJokEfkYCUUQhR6b70m8PcuKFZdrYsMfWRAKsaIjzWUdzd7eQoEEKIKciiOyEq22ja4ts7unj55javQxFTuHx1Az9+/mRR71OSYiGEmILfNGiIyE53QlSqrduPcWF7HS21Ia9DEVO4el0jP9t1CssuXgmFJMVCCDGNurAfv/QtFaLiZCyHex/u4NUXLfc6FDGNtliIltogjx4qXgmFjPZCCDENpRTNNUGvwxBCFNh3nu5iRX2Yc1pqvA5FzOCa9U3ct6OraPfnSVKslHqzUmqXUspRSl057udrlVJJpdTO3Nd/ehGfEELkhQMmNUFZdCdEpchYDnf94gC/dWm716GIWVx/bjMP7e1mKJktyv15NVP8AvBG4OEpfndIa31p7usPihyXEEKcpTEawJBFd0JUhK8/cZTldSHOW1brdShiFrGQn4tX1vGdp4szW+xJUqy13qO13ufFfQshxHz5TIOGqPQuFqLcDSWz3P2LA7zlylVehyLm6OXnt/Hl3xzBcfSS31cp1hSvU0o9o5T6lVLqxdNdSCn1fqXUU0qpp3p6eooZnxAzkmOzMtWF/WXdy1SOS1GKin1cfubn+7h8dQNrmqJLfl+iMM5bVovPNPjF3u4lv68lG+GVUg8qpV6Y4uvWGa52Elittb4M+DPgf5RSsakuqLW+V2t9pdb6ypYW2Z5RlA45NitXOS+6k+NSlKJiHpfPdw3x/Z0neMtVMktcTpRSvPbi5Xz2wf1ovbSzxUu2ekRrffMCrpMG0rl/71BKHQI2Ak8VODwhhJi3kN+kJiSL7oQoN2nL5s++uZO3Xb2aWEj6j5ebq9Y28v2dJ/jprlPccuHStdErqXOBSqkWpZSZ+/d6YAPQ4W1UQghxRlM0iGnIojshysknf7yHppoAN5zb7HUoYgEMpbjtqlX83Y/2kMraS3c/S3bLM1BKvUEp1QVcC/xIKfXT3K9uBJ5TSj0LfBv4A611vxcxCiHEVExDUReWmSYhysX3dx7nJy+c5r3Xr5et28vYxSvrWdUY4bMP7l+y+/Cq+8R3tdYrtdZBrXWb1vqVuZ/fp7W+QGt9idb6cq31D7yITwghZiIzxUKUh8c7+vi/9+/iT27eIKVPFeBd16zhm0918dgS7XJXUuUTQgghhBCF8HhHH3/wtR18YMu50m2iQtRHAtx+43r+6H+e5lhfouC3L0mxEEIIISrKfTu6xhLiC9vrvA5HFNDFK+u59bIVvO3zj9PZX9jEWM4lCCGEEKIi9Mcz/O0PdvHUkQH+6lXns6ox4nVIYgm8/PxlaA1v+Nyj/OvbLufac5oKcruSFAshhBCirA3EM3z1saN88dHDXHduE3/3WxcS8ptehyWW0Cs2L6OtNsQf/c/TvHxzG3/68o20xUKLuk1JioUQQghRVrTWdPYnefxwHz/bdZrHO/q4am0DH33tZlbUh70OTxTJJavq+dRvX8z3dh7n5n/5FS8+t5nXX7qCa9c3UxeZf5cgSYqFEEIIUdK++tgRfvz8KU4Np+gaSJC13Z3NlsVCXLyyjjtetoFowCSVtenoGfU4WlFs161v4uL2Oh471McHv/0cwymLV1+0jM+9/Yp53Y5a6i3zikEp1QMc9TqOcZqBXq+DmEEpx1fKsQGEtNYXzvXCBT42S+FvIzGUZgy9Wutb5nrF3HEZx7vH4OXfT+67uOZ8bM40Xi57xz9uDLafX5v/bzs+mHVSo1aBYiwYrR2fUkbJxZVXLfEpf9BIdjw92P/Tf+2a5iJTHpcVkRSXGqXUU1rrK72OYzqlHF8pxwbexlcKfxuJoXJiqNZjWe67/JXyYynl2EDim420ZBNCCCGEEFVPkmIhhBBCCFH1JCleGvd6HcAsSjm+Uo4NvI2vFP42EoOrEmKo1mNZ7rv8lfJjKeXYQOKbkdQUCyGEEEKIqiczxUIIIYQQoupJUiyEEEIIIaqeJMVLRCn1j0qpvUqp55RS31VK1ZdATLcopfYppQ4qpT7sdTzjKaVWKaV+qZTao5TapZS60+uYJlNKmUqpZ5RSP/To/j07pkrh2CmVY8Tr4yAXQ71S6tu542GPUuraOVznb5RSx5VSO3Nfr57mcgV/rud67Cqljiilns/F99Qi73PGx6Fcd+d+/5xS6vLF3N+42531OFVKbVFKDY17Lj5WiPvO3faMf8OletxLTSn15tzf01FKXTnpdx/JPZ59SqlXehXjuHjm9FortlIYx2dSyNf/gmmt5WsJvoBXAL7cvz8NfNrjeEzgELAeCADPApu9/juNi285cHnu37XA/lKKLxfXnwH/A/zQo/v35JgqlWOnVI4Rr4+DXAxfBt6X+3cAqJ/Ddf4G+Asvnuu5HrvAEaC5APc36+MAXg08ACjgGuCJAj03sx6nwJalOn5m+xsu1eNe6i/gfOA8YBtw5bifb849v0FgXe55Nz2OddbXmgcxlcQ4PkuMBXn9L+ZLZoqXiNb6Z1rr/K4sjwMrvYwHuBo4qLXu0FpngK3ArR7HNEZrfVJr/XTu3yPAHqDd26jOUEqtBF4DfMGrGDw8pkri2CmFY6QUjgOlVAy4EfgvAK11Rms9WKCbX5Ln2oNjdy6P41bgK9r1OFCvlFq+2DsuheN0FkvyuJea1nqP1nrfFL+6FdiqtU5rrQ8DB3GffzFRSYzjpU6S4uL4PdxP5l5qBzrH/XcXpTVQj1FKrQUuA57wOJTxPgt8EHA8jiOvmMdUyR07Hh4jn8X742A90AP8d66M4wtKqegcr/tHuVPmX1RKNUzx+2I81zMduxr4mVJqh1Lq/Yu4j7k8jiV/rLMcp9cqpZ5VSj2glLqggHc729+w5F7Pi1Sqj2e211qxlerfabxCvf4XzOfFnVYKpdSDwLIpfvV/tNbfz13m/wAW8PVixjYFNcXPSq4fn1KqBrgP+BOt9bDX8QAopV4LdGutdyiltizxfZXiMVVSx45Xx0gxj4NZ+IDLgT/WWj+hlLoL+DDw0ZmOH+A/gE/gPnefAP4ZN0Edb8HPdYGO3eu11ieUUq3Az5VSe7XWD8/l/ieHM8XPJj+OJT2uZzlOnwbWaK1Hc/Wm3wM2FOiuZ/sbltTreby5HENTXW2Kny354ynAa63YSvZ5H6dQr/8Fk6R4EbTWN8/0e6XU7wKvBV6mcwUzHuoCVo3775XACY9imZJSyo/7JvJ1rfV3vI5nnOuB1+fevEJATCn1Na31Owp9RyV6TJXMsePxMVK042AWXUCX1jo/+/ht3KR41uMnTyn1eWCqhYILfq4LcexqrU/kvncrpb6Le8p3IW+Kc3kcS3Zcz3acjk+StdY/Vkp9TinVrLXuXex9z+FvWDKv58nmevxO4snjKcBrrdhK9nnPK+Drf8GkfGKJKKVuAT4EvF5rnfA6HuBJYINSap1SKgDcBtzvcUxjlFIKt0Zyj9b6X7yOZzyt9Ue01iu11mtx/24PeZAIeXlMlcSx4/UxUirHgdb6FNCplDov96OXAbtnu96kutE3AC9McbElea7ncuwqpaJKqdr8v3EX500V41zM5XHcD7wr143hGmBIa31ygfc3Zi7HqVJqWe5yKKWuxn0v7ivAfc/lb7gkj9tD9wO3KaWCSql1uDPu270MaI6vtWIriXF8OgV+/S+YzBQvnX/DXQ3789zY97jW+g+8CkZrbSml/gj4Ke4q1C9qrXd5Fc8UrgfeCTyvlNqZ+9lfaa1/7F1IJceTY6qEjh05Rs74Y+DruTe3DuA9c7jOPyilLsU9ZXoEuB1AKbUC+ILW+tVL+FxPeeyOv2+gDfhu7vc+4H+01j9ZyJ1N9ziUUn+Q+/1/Aj/G7cRwEEgwt7/hXEx5nAKrx933m4A/VEpZQBK4rUBnfqb8GxbpcS8ppdQbgH8FWoAfKaV2aq1fmXtev4n7wdACPqC1tr2MlWlea14qoXF8OgV7/S+GbPMshBBCCCGqnpRPCCGEEEKIqidJsRBCCCGEqHqSFAshhBBCiKonSbEQQgghhKh6khQLIYQQQoiqJ0lxhcttMdmtlJqx359SaotS6rpixSWqk1JqlVLql0qpPUqpXUqpO+d5/W1KqSuXKj5R3ZRSIaXUduVuv7xLKfW3c7jO2tnGVyEKQSllKndr9x/m/vvdubaG+d8fUUo1exdh+ZOkuPJ9CbhlDpfbAkhSLJaaBfy51vp84BrgA0qpzR7HJEReGrhJa30JcClwS26DizFKKenvL7xyJ7Bn3H+/G1gx9UXFQkhSXOFy+4b3j/+ZUuoOpdRupdRzSqmtSqm1wB8Af6qU2qmUerEXsYrKp7U+qbV+OvfvEdwBvj03A/zp3Czd/vwxqJQK547R55RS/wuEPQxfVDjtGs39pz/3pXPH5/9TSv0KuFMpdUVuNvkx4AOeBSyqhlJqJfAa4Au5/34TcCXuJj47lVL5sfGPlVJPK6WeV0pt8ijcsiWfeKvTh4F1Wuu0Uqpeaz2olPpPYFRr/U9eByeqQ+7D2GXAE7kf+bTWVyulXg38X+Bm4A+BhNb6YqXUxcDTngQrqoZSygR2AOcC/661fiK3y1a91volucs8B/yx1vpXSql/9C5aUUU+C3wQqAXQWn87t0PdX2itnwLIHae9WuvLlVL/H/AXwPu8Cbc8yUxxdXoO99PlO3BPZwtRVEqpGuA+4E+01sO5H38n930HsDb37xuBrwForZ/DPXaFWDJaa1trfSmwErhaKXVh7lf/C6CUqsNNkH+V+/lXix+lqCZKqdcC3VrrHXO4+FTjqJgjSYqr02uAfweuAHZIjZwoJqWUHzch/rrW+jvjfpXOfbeZeBZL9qIXRae1HgS2cWZNRjz3XSHHpCiu64HXK6WOAFuBm5RSX5vmstONo2IOJCmuMkopA1iltf4l7qmYeqAGGCF3WkaIpaLc83v/BezRWv/LHK7yMPD23HUvBC5ewvBElVNKtSil6nP/DuOW8Owdf5lcsjyklLoh96O3FzNGUX201h/RWq/UWq8FbgMe0lq/A3nfLjhJiiucUuobwGPAeUqpLuD3ga8ppZ4HngE+kxvkfwC8QRbaiSV2PfBO3JmOnbmvV89w+f8AanI1nB8EthcjSFG1lgO/zB1vTwI/11r/cIrLvQf499xCu2QxAxRinC8B/zlpoZ1YBKW1nAUSQgghhBDVTWaKhRBCCCFE1ZOkWAghhBBCVD1JioX4/9utAwEAAAAAQf7Wg1wUAQB7UgwAwJ4UAwCwJ8UAAOxJMQAAewE+9lrYuCp6UQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_depth_ns_hermite4.iloc[:,2:],vars=['1st','2nd','3rd','4th'],markers='o', kind='reg',diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can check the arithmetic averages of all the hermite basis expansions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The means of each basis expansion, 0 - 4th order = [ 1.          0.00536486 -0.0541238   0.05726848 -0.36447919].\n"
     ]
    }
   ],
   "source": [
    "print('The means of each basis expansion, 0 - 4th order = ' + str(stats.describe(depth_ns_hermite4)[2]) + '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize Hermite polynomials over the range of the standardized depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "plt.plot(depth_ns_values, scipy.special.eval_hermite(orders4, depth_ns_values.reshape(-1, 1)) [:,0], label='0th', color = 'black')\n",
    "plt.plot(depth_ns_values, scipy.special.eval_hermite(orders4, depth_ns_values.reshape(-1, 1)) [:,1], label='1st', color = 'blue')\n",
    "plt.plot(depth_ns_values, scipy.special.eval_hermite(orders4, depth_ns_values.reshape(-1, 1)) [:,2], label='2nd', color = 'green')\n",
    "plt.plot(depth_ns_values, scipy.special.eval_hermite(orders4, depth_ns_values.reshape(-1, 1)) [:,3], label='3rd', color = 'red')\n",
    "plt.plot(depth_ns_values, scipy.special.eval_hermite(orders4, depth_ns_values.reshape(-1, 1)) [:,4], label='4th', color = 'orange')\n",
    "plt.title('Hermite Expansion vs Standardized Depth')\n",
    "plt.xlabel('Standard Normal Depth')\n",
    "plt.ylabel('h(x)')\n",
    "plt.legend(); plt.ylim(-500,500); plt.xlim(-3,3)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's fit our Hermite basis regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_herm4 = LinearRegression() \n",
    "lin_herm4.fit(df_depth_ns_hermite4.iloc[:,1:], Npor_ns) \n",
    "plt.subplot(111)\n",
    "plt.plot(depth_ns_values, lin_herm4.predict(scipy.special.eval_hermitenorm(orders4, depth_ns_values.reshape(-1, 1), out=None)), label='4th order',color = 'red') \n",
    "plt.plot(depth_ns, Npor_ns, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.title('Hermite Polynomial: Standardized Porosity vs Standardized Depth')\n",
    "plt.xlabel('Standardized Depth')\n",
    "plt.ylabel('Standardized Porosity')\n",
    "plt.xlim(-2,2); plt.ylim(-2,2)\n",
    "plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have less correlation between the expanded basis features we can check out the model coefficients and interpret the unique importance of each order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.56927068, -0.42466205,  0.15226789, -0.09614024])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_herm4.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Orthogonal Polynomials\n",
    "\n",
    "Let's try the orthogonal polygons reimplimented in Python by Dave Moore from the poly() function in R.\n",
    "\n",
    "* the functions below for fit and predict are directly from Dave's [blog](http://davmre.github.io/blog/python/2013/12/15/orthogonal_poly)\n",
    "\n",
    "* note during the fit to the training data the norm2 and alpha model parameters are cacluated\n",
    "* these parameters must be passed to each subsequent predict to ensure the results are consistent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np  \n",
    "\n",
    "# functions taken (without modification) from http://davmre.github.io/blog/python/2013/12/15/orthogonal_poly\n",
    "# appreciation to Dave Moore for the great blog post on titled 'Orthogonal polynomial regression in Python'\n",
    "# functions are Dave's reimplimentation of poly() from R\n",
    "\n",
    "def ortho_poly_fit(x, degree = 1):\n",
    "    n = degree + 1\n",
    "    x = np.asarray(x).flatten()\n",
    "    if(degree >= len(np.unique(x))):\n",
    "            stop(\"'degree' must be less than number of unique points\")\n",
    "    xbar = np.mean(x)\n",
    "    x = x - xbar\n",
    "    X = np.fliplr(np.vander(x, n))\n",
    "    q,r = np.linalg.qr(X)\n",
    "\n",
    "    z = np.diag(np.diag(r))\n",
    "    raw = np.dot(q, z)\n",
    "\n",
    "    norm2 = np.sum(raw**2, axis=0)\n",
    "    alpha = (np.sum((raw**2)*np.reshape(x,(-1,1)), axis=0)/norm2 + xbar)[:degree]\n",
    "    Z = raw / np.sqrt(norm2)\n",
    "    return Z, norm2, alpha\n",
    "\n",
    "def ortho_poly_predict(x, alpha, norm2, degree = 1):\n",
    "    x = np.asarray(x).flatten()\n",
    "    n = degree + 1\n",
    "    Z = np.empty((len(x), n))\n",
    "    Z[:,0] = 1\n",
    "    if degree > 0:\n",
    "        Z[:, 1] = x - alpha[0]\n",
    "    if degree > 1:\n",
    "        for i in np.arange(1,degree):\n",
    "             Z[:, i+1] = (x - alpha[i]) * Z[:, i] - (norm2[i] / norm2[i-1]) * Z[:, i-1]\n",
    "    Z /= np.sqrt(norm2)\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's give it a try and perform orthogonal polynomial expansion of our standard normal transformed depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>0th</th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "      <th>3rd</th>\n",
       "      <th>4th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.026808</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>-0.330385</td>\n",
       "      <td>0.440404</td>\n",
       "      <td>-0.460160</td>\n",
       "      <td>0.420374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.780464</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>-0.290335</td>\n",
       "      <td>0.313201</td>\n",
       "      <td>-0.207862</td>\n",
       "      <td>0.021278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.534121</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>-0.250285</td>\n",
       "      <td>0.202153</td>\n",
       "      <td>-0.029761</td>\n",
       "      <td>-0.172968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-1.356312</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>-0.221377</td>\n",
       "      <td>0.132038</td>\n",
       "      <td>0.058235</td>\n",
       "      <td>-0.220834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.213340</td>\n",
       "      <td>0.158114</td>\n",
       "      <td>-0.198133</td>\n",
       "      <td>0.081765</td>\n",
       "      <td>0.107183</td>\n",
       "      <td>-0.219084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      value       0th       1st       2nd       3rd       4th\n",
       "0 -2.026808  0.158114 -0.330385  0.440404 -0.460160  0.420374\n",
       "1 -1.780464  0.158114 -0.290335  0.313201 -0.207862  0.021278\n",
       "2 -1.534121  0.158114 -0.250285  0.202153 -0.029761 -0.172968\n",
       "3 -1.356312  0.158114 -0.221377  0.132038  0.058235 -0.220834\n",
       "4 -1.213340  0.158114 -0.198133  0.081765  0.107183 -0.219084"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_ns_ortho4, norm2, alpha = ortho_poly_fit(depth_ns.values.reshape(-1, 1), degree = 4)\n",
    "df_depth_ns_ortho4 = pd.DataFrame({'value':depth_ns.values,'0th': depth_ns_ortho4[:,0],'1st': depth_ns_ortho4[:,1],'2nd': depth_ns_ortho4[:,2], '3rd': depth_ns_ortho4[:,3], '4th': depth_ns_ortho4[:,4]}) # make a new DataFrame from the vectors\n",
    "df_depth_ns_ortho4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the correlation between the orthogonal polynomial predictor features. I'm impressed! The between basis feature order correlations are all zero!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>1st</th>\n",
       "      <th>2nd</th>\n",
       "      <th>3rd</th>\n",
       "      <th>4th</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1st</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>-1.387779e-16</td>\n",
       "      <td>2.775558e-17</td>\n",
       "      <td>-2.775558e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd</th>\n",
       "      <td>-1.387779e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>-2.775558e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3rd</th>\n",
       "      <td>2.775558e-17</td>\n",
       "      <td>5.551115e-17</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.498002e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4th</th>\n",
       "      <td>-2.775558e-17</td>\n",
       "      <td>-2.775558e-17</td>\n",
       "      <td>2.498002e-16</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              1st           2nd           3rd           4th\n",
       "1st  1.000000e+00 -1.387779e-16  2.775558e-17 -2.775558e-17\n",
       "2nd -1.387779e-16  1.000000e+00  5.551115e-17 -2.775558e-17\n",
       "3rd  2.775558e-17  5.551115e-17  1.000000e+00  2.498002e-16\n",
       "4th -2.775558e-17 -2.775558e-17  2.498002e-16  1.000000e+00"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_depth_ns_ortho4.iloc[:,2:].corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the bivariate relationships between our orthogonal polynomial basis orders. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x27fbea8a640>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_depth_ns_ortho4.iloc[:,2:],vars=['1st','2nd','3rd','4th'],markers='o', kind='reg',diag_kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize orthogonal polynomial basis orders over the range of the standardized depth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ortho_poly_ns_values = ortho_poly_predict(depth_ns_values.reshape(-1, 1), alpha, norm2, degree = 4)\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.plot(depth_ns_values, ortho_poly_ns_values[:,0], label='0th', color = 'black')\n",
    "plt.plot(depth_ns_values, ortho_poly_ns_values[:,1], label='1st', color = 'blue')\n",
    "plt.plot(depth_ns_values, ortho_poly_ns_values[:,2], label='2nd', color = 'green')\n",
    "plt.plot(depth_ns_values, ortho_poly_ns_values[:,3], label='3rd', color = 'red')\n",
    "plt.plot(depth_ns_values, ortho_poly_ns_values[:,4], label='4th', color = 'orange')\n",
    "plt.title('Orthogonal Polygon Expansion vs Standardized Depth')\n",
    "plt.xlabel('Density (g/cm3)')\n",
    "plt.ylabel('h(x)')\n",
    "plt.legend(); plt.ylim(-.5,.5); plt.xlim(-3,3)\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let's fit our orthogonal polynomial basis expansion regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lin_ortho4 = LinearRegression() \n",
    "lin_ortho4.fit(df_depth_ns_ortho4.iloc[:,1:], Npor_ns) \n",
    "plt.subplot(111)\n",
    "\n",
    "plt.plot(depth_ns_values, lin_ortho4.predict(ortho_poly_ns_values), color = 'blue', label = 'orthogonal polynomial') \n",
    "plt.plot(depth_ns, Npor_ns, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.title('Orthogonal Polynomial: Porosity vs Density')\n",
    "plt.xlabel('Density (g/cm3)')\n",
    "plt.ylabel('Porosity (%)')\n",
    "plt.xlim(-2,2); plt.ylim(-2,2)\n",
    "plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check out the model parameters for our independent orthogonal basis predictor features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lin_ortho4.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Polynomial Regression in scikit-learn with Pipelines\n",
    "\n",
    "The need to first perform basis expansion and then train the resulting (after basis transformations) linear model may seem a bit complicated.\n",
    "\n",
    "* one solution is to use the Pipeline object from scikit-learn. \n",
    "\n",
    "Here's some highlights on Pipelines.\n",
    "\n",
    "###  Machine Learning Modeling Pipelines\n",
    "\n",
    "Machine learning workflows can be complicated, with various steps:\n",
    "\n",
    "* data preparation, feature engineering transformations\n",
    "\n",
    "* model parameter fitting\n",
    "\n",
    "* model hyperparameter tuning\n",
    "\n",
    "* modeling method selection\n",
    "\n",
    "* searching over a large combinatorial of hyperparameters\n",
    "\n",
    "* training and testing model runs\n",
    "\n",
    "Pipelines are a scikit-learn class that allows for the encapsilation of a seuqence of data preparation and modeling steps\n",
    "\n",
    "* then we can treat the pipeline as an object in our much condensed workflow\n",
    "\n",
    "The pipeline class allows us to:\n",
    "\n",
    "* improve code readability and to keep everything straight\n",
    "\n",
    "* avoid common workflow problems like data leakage, testing data informing model parameter training \n",
    "\n",
    "* abstract common machine learning modeling and focus on building the best model possible\n",
    "\n",
    "The fundamental philosophy is to treat machine learning as a combinatorial search to find the best model (AutoML)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "order=5                                                 # set the polynomial order\n",
    "\n",
    "polyreg=make_pipeline(PolynomialFeatures(order),LinearRegression()) # make the modeling pipeline\n",
    "polyreg.fit(depth_ns.values.reshape(-1, 1), Npor_ns)    # fit the model to the data\n",
    "Npor_hat = polyreg.predict(depth_ns_values.reshape(-1, 1)) # predict with the modeling pipeline\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.plot(depth_ns_values,Npor_hat, label='4th order',color = 'red') \n",
    "plt.plot(depth_ns, Npor_ns, 'o', label='sample data', color = 'darkorange', alpha = 0.8, markeredgecolor = 'black')\n",
    "plt.title('Trained Polynomial Moder of Order = ' + str(order))\n",
    "plt.xlabel('Standardized Depth')\n",
    "plt.ylabel('Standardized Porosity')\n",
    "plt.xlim(-2,2); plt.ylim(-2,2)\n",
    "plt.legend()\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "Polynomial regression is a flexible method for modeling nonlinear data and it introduces the concept of basis expansion. \n",
    "\n",
    "* We could have done more to explore the advantages of orthogonal basis vs. nonorthogonal basis. \n",
    "\n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On twitter I'm the @GeostatsGuy.\n",
    "\n",
    "\n",
    "***\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Geostatistics:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n"
   ]
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